U.S. patent number 8,406,498 [Application Number 12/631,795] was granted by the patent office on 2013-03-26 for blood and cell analysis using an imaging flow cytometer.
This patent grant is currently assigned to Amnis Corporation. The grantee listed for this patent is David Basiji, Thaddeus George, Brian Hall, Philip Morrissey, William Ortyn, David Perry, Cathleen Zimmerman. Invention is credited to David Basiji, Thaddeus George, Brian Hall, Philip Morrissey, William Ortyn, David Perry, Cathleen Zimmerman.
United States Patent |
8,406,498 |
Ortyn , et al. |
March 26, 2013 |
Blood and cell analysis using an imaging flow cytometer
Abstract
Multimodal or multispectral images of cells comprising a
population of cells are simultaneously collected. Photometric
and/or morphometric image features identifiable in the images are
used to identify differences between first and second populations
of cells. The differences can include changes in a relative
percentage of different cell types in each population, or a change
in a first type of cell present in the first population of cells
and the same type of cell in the second population of cells. The
changes may be indicative of a disease state, indicative of a
relative effectiveness of a therapy, or indicative of a health of
the person from whom the cells populations were obtained.
Inventors: |
Ortyn; William (Bainbridge
Island, WA), Basiji; David (Seattle, WA), Morrissey;
Philip (Bellevue, WA), George; Thaddeus (Seattle,
WA), Hall; Brian (Seattle, WA), Zimmerman; Cathleen
(Bainbridge Island, WA), Perry; David (Woodinville, WA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Ortyn; William
Basiji; David
Morrissey; Philip
George; Thaddeus
Hall; Brian
Zimmerman; Cathleen
Perry; David |
Bainbridge Island
Seattle
Bellevue
Seattle
Seattle
Bainbridge Island
Woodinville |
WA
WA
WA
WA
WA
WA
WA |
US
US
US
US
US
US
US |
|
|
Assignee: |
Amnis Corporation (Seattle,
WA)
|
Family
ID: |
42730742 |
Appl.
No.: |
12/631,795 |
Filed: |
December 4, 2009 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20100232675 A1 |
Sep 16, 2010 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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12362170 |
Jan 29, 2009 |
7634126 |
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11344941 |
Feb 1, 2006 |
7522758 |
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11123610 |
May 4, 2005 |
7450229 |
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10628662 |
Jul 28, 2003 |
6975400 |
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09976257 |
Oct 12, 2001 |
6608682 |
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09820434 |
Mar 29, 2001 |
6473176 |
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09538604 |
Mar 29, 2000 |
6211955 |
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09490478 |
Jan 24, 2000 |
6249341 |
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60649373 |
Feb 1, 2005 |
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60567911 |
May 4, 2004 |
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60117203 |
Jan 25, 1999 |
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60240125 |
Oct 12, 2000 |
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Current U.S.
Class: |
382/133 |
Current CPC
Class: |
A61B
1/00188 (20130101); G01N 15/1475 (20130101); G06K
9/0014 (20130101); G01N 15/147 (20130101); G01N
2015/1488 (20130101) |
Current International
Class: |
G06K
9/00 (20060101) |
Field of
Search: |
;382/128,133,134
;356/39 |
References Cited
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|
Primary Examiner: Johns; Andrew W
Attorney, Agent or Firm: Lee & Hayes, PLLC
Government Interests
GOVERNMENT RIGHTS
This invention was funded at least in part with a grant (No. R43 CA
94590-01) from the National Cancer Institute, and the U.S.
government may have certain rights in this invention.
Parent Case Text
RELATED APPLICATIONS
This application is a continuation in part application based on
prior copending patent application Ser. No. 12/362,170, filed on
Jan. 29, 2009, which itself is a divisional application based on
prior patent application Ser. No. 11/344,941, filed on Feb. 1,
2006, now U.S. Pat. No. 7,522,758, the benefit of the filing date
of which is hereby claimed under 35 U.S.C. .sctn.120. Patent
application Ser. No. 11/344,941 is based on a prior provisional
application Ser. No. 60/649,373, filed on Feb. 1, 2005, the benefit
of the filing date of which is hereby claimed under 35 U.S.C.
.sctn.119(e). Patent application Ser. No. 11/344,941 is also a
continuation-in-part application based on a prior conventional
application Ser. No. 11/123,610, filed on May 4, 2005, which issued
as U.S. Pat. No. 7,450,229 on Nov. 11, 2008, which itself is based
on a prior provisional application Ser. No. 60/567,911, filed on
May 4, 2004, and which is also a continuation-in-part of prior
patent application Ser. No. 10/628,662, filed on Jul. 28, 2003,
which issued as U.S. Pat. No. 6,975,400 on Dec. 13, 2005, which
itself is a continuation-in-part application of prior patent
application Ser. No. 09/976,257, filed on Oct. 12, 2001, which
issued as U.S. Pat. No. 6,608,682 on Aug. 19, 2003, which itself is
a continuation-in-part application of prior patent application Ser.
No. 09/820,434, filed on Mar. 29, 2001, which issued as U.S. Pat.
No. 6,473,176 on Oct. 29, 2002, which itself is a
continuation-in-part application of prior patent application Ser.
No. 09/538,604, filed on Mar. 29, 2000, which issued as U.S. Pat.
No. 6,211,955 on Apr. 3, 2001, which itself is a
continuation-in-part application of prior patent application Ser.
No. 09/490,478, filed on Jan. 24, 2000, which issued as U.S. Pat.
No. 6,249,341 on Jun. 19, 2001, which itself is based on prior
provisional patent application Ser. No. 60/117,203, filed on Jan.
25, 1999, the benefit of the filing dates of which is hereby
claimed under 35 U.S.C. .sctn.120 and 35 U.S.C. .sctn.119(e).
Patent application Ser. No. 09/976,257, noted above, is also based
on prior provisional application Ser. No. 60/240,125, filed on Oct.
12, 2000, the benefit of the filing date of which is hereby claimed
under 35 U.S.C. .sctn.119(e).
Claims
The invention in which an exclusive right is claimed is defined by
the following:
1. A method for detecting at least one difference between a first
and a second population of cells using image data collected
separately for each population, where the image data include a
plurality of images of individual cells that are acquired
simultaneously, for the first population and for the second
population, comprising the steps of: (a) imaging the first
population of cells and the second population of cells to collect
the image data, such that a plurality of images for the cells are
simultaneously collected for cells in the first population and for
cells in the second population, the plurality of images comprising
at least one of the following two types of images: (i)
multispectral images; and (ii) multimodal images; and (b) analyzing
the image data collected to identify at least one difference
between the image data collected for the first and the second
population of cells.
2. The method of claim 1, further comprising the step of
determining whether the difference between the first and second
population of cells is indicative of a health of a person from
which the first and second population of cells were obtained.
3. The method of claim 1, wherein the step of analyzing the image
data collected to identify at least one difference between the
first and the second population of cells comprises the step of
identifying at least one morphometric image feature that differs
between the first and the second population of cells.
4. The method of claim 1, wherein the step of analyzing the image
data collected to identify at least one difference between the
first and second population of cells comprises the step of
identifying at least one photometric image feature that differs
between the first and the second population of cells.
5. The method of claim 1, wherein the step of analyzing the image
data collected to identify at least one difference between the
first and second population of cells comprises the step of
identifying at least one cell type present in one of the first and
second population of cells, but not in the other of the first and
the second population of cells.
6. The method of claim 1, wherein the step of analyzing the image
data collected to identify at least one difference between the
first and the second population of cells comprises the step of
quantifying a difference between a distribution of cell types in
the first and the second population of cells.
7. The method of claim 1, wherein the step of analyzing the image
data collected to identify at least one difference between the
first and the second population of cells comprises the step of
quantifying a difference between a first type of cell present in
the first population of cells and the same type of cell present in
the second population of cells.
8. The method of claim 1, further comprising the step of adding a
reagent to at least one of the first and the second population of
cells before imaging that population of cells, wherein the reagent
comprises at least one reagent selected from the group consisting
of: (a) a label that facilitates identification of one or more
cellular biomolecules; and (b) a stimulus likely to induce a change
in the population of cells exposed to the stimulus.
9. The method of claim 1, wherein the step of imaging the first and
the second population of cells to collect the image data comprises
the step of simultaneously collecting at least two types of images
for a cell, selected from a group consisting of the following types
of images: a bright field image, a dark field image, and a
fluorescence image.
10. The method of claim 1, further comprising the step of acquiring
the first population of cells from a person at a first time, and
acquiring the second population of cells from the person at a later
time.
11. The method of claim 1, further comprising the step of exposing
the second population of cells to a stimulus without exposing the
first population of cells to the same stimulus, before collecting
the image data for the second population of cells.
12. The method of claim 1, wherein the difference quantified
between the first and second population of cells is a distribution
of a molecule within the cells of each sample.
13. A method for detecting at least one difference between a first
and a second population of cells using image data collected
separately for each population, where the image data include a
plurality of images of individual cells that are acquired
simultaneously, for the first population and for the second
population, comprising the steps of: (a) acquiring the first
population of cells and the second population of cells from a
person at the same time; (b) imaging the first population of cells
and the second population of cells to collect the image data, such
that a plurality of images for the cells are simultaneously
collected for cells in the first population and for cells in the
second population, the plurality of images comprising at least one
of the following two types of images: (i) multispectral images; and
(ii) multimodal images; and (c) analyzing the image data collected
to identify at least one difference between the image data
collected for the first and the second population of cells.
14. A method for detecting at least one difference between a first
and a second population of cells using image data collected
separately for each population, where the image data include a
plurality of images of individual cells that are acquired
simultaneously, for the first population and for the second
population, comprising the steps of: (a) exposing the second
population of cells to a stimulus without exposing the first
population of cells to the same stimulus (b) after exposing the
second population of cells to the stimulus, imaging the first
population of cells and the second population of cells to collect
the image data, such that a plurality of images for the cells are
simultaneously collected for cells in the first population and for
cells in the second population, the plurality of images comprising
at least one of the following two types of images: (i)
multispectral images; and (ii) multimodal images; and (c) analyzing
the image data collected to identify at least one difference
between the image data collected for the first and the second
population of cells.
Description
BACKGROUND
Cellular hematopathologies have been traditionally identified and
studied by a variety of slide based techniques that include
morphological analysis of May-Grunwald/Giemsa or Wright/Giemsa
stained blood films and cytoenzymology. Additionally, other
techniques, such as cell population analysis by flow cytometry, and
molecular methods, such as polymerase chain reaction (PCR) or in
situ hybridization to determine gene expression, gene mutations,
chromosomal translocations and duplications, have added to the
understanding of these pathologies.
Although progress has been made using such techniques in advancing
diagnostic capabilities, understanding the mechanisms and the
progression of disease, as well as evaluating new therapeutics,
such technologies each offer challenges with regard to
standardization and robustness, and to a large degree, they have
not yet evolved to become routine laboratory tests.
The conventional hematology clinical laboratory includes
technologies to rapidly and automatically analyze large numbers of
samples of peripheral blood, with minimal human intervention.
Companies such as Abbott Laboratories (Abbott Park, Ill.), Beckman
Coulter Inc. (Fullerton, Calif.), and TOA Corporation (Kobe, Japan)
continue to advance these technologies with regard to throughput
levels, the degree of accuracy of the analysis, as well as
moderately increasing the information content gathered in each
sample run. However, in regard to any sample suggestive of a
cellular hematopathology, i.e., falling outside the accepted degree
of variance for any particular parameter, traditional slide based
methodologies are largely used to determine the probable cause of
the abnormality.
Diagnostic criteria in hematology are based on the morphological
identification of abnormalities in cell numbers, size, shape and
staining patterns. Although these have been supplemented over the
past decades with cell population analysis, by staining with
monoclonal antibodies to various cell surface determinants and
acquiring data via flow cytometry, the most important element in
the diagnostic evaluation is the visual inspection of the
peripheral blood film, bone marrow and lymph node biopsy using a
microscope, which enables a subjective categorization of putative
abnormalities.
The manual evaluation of tissue and blood films from patients is
tedious, time consuming, and subject to significant
intra-laboratory and intra-observer variability. This process
suffers from many sources of variability and error, including
staining variability (which adversely affects longitudinal
analysis), bias of the evaluator, and suboptimal sample preparation
(blood films with increased "smudge" cells and atypical
lymphocytes). The manual classification of a few hundred cells by
morphological appearance results in poor statistical power and low
confidence in evaluating observed changes over time, or as a result
of treatment.
Chronic lymphocytic leukemia (CLL) is a type of cancer in which the
bone marrow produces an excess of lymphocytes (a type of white
blood cell) due to a malignant transformation event (e.g.,
chromosomal translocation). CLL is the most frequent type of
leukemia in the Western world. Normally, stem cells (immature
cells) develop into mature blood cells by a process of ordered
differentiation, which occurs in the bone marrow. There are three
types of mature blood cells: (1) red blood cells that carry oxygen
to all tissues of the body; (2) white blood cells that fight
infection; and, (3) platelets that help prevent bleeding by forming
blood clots. Normally, the numbers and types of these blood cells
are tightly regulated. In CLL, there is a chronic pathological
overproduction of a type of white blood cell called lymphocytes.
There are three types of lymphocytes: (1) B lymphocytes that make
antibodies to help fight infection; (2) T lymphocytes that help B
lymphocytes make antibodies to fight infection; and, (3) killer
cells that attack cancer cells and viruses. CLL is a disease
involving an increase in B lymphocyte cell numbers in the
peripheral blood, usually reflective of a clonal expansion of a
malignantly transformed CD5+ B lymphocyte cell.
Currently, established chemotherapeutic treatments are used to
treat this condition, but a number of newer therapeutics, involving
monoclonal antibodies to cell surface antigens expressed on CLL
cells (e.g., Rituximab), have been developed. Recent data from the
National Cancer Data Base indicate that the 5-year survival for
this disease condition is about 48%, with only 23% of patients
surviving the disease condition after 10 years. Recently, a number
of prognostic factors have been identified that allow
stratification of the patient population into two subpopulations
with distinct clinical outcomes. Factors that tend to correlate
with decreased survival are: ZAP70 expression (a tyrosine kinase
required for T lymphocyte cell signaling), increased CD38
expression, un-mutated Ig Vh genes, and chromosomal abnormalities.
However, routine assessment of these factors has not evolved to a
standard clinical practice, due to technical challenges with data
standardization and interpretation.
Morphological evaluation remains the "gold standard" in the
assessment of hematopathologies, and patients with CLL present with
morphological heterogeneity. Attempts to correlate a particular
morphological profile with clinical prognosis have been made, but
to date, no association has been widely accepted, and the
morphologic sub-classification of CLL and its correlation with
clinical prognosis remains to be explored.
It would therefore be desirable to provide a method and apparatus
suitable for automatically analyzing blood, including peripheral
blood leukocytes, and cellular components such as bone marrow and
lymph nodes (whose cells are readily amenable to being processed in
suspension), to facilitate researching blood related diseases and
abnormalities. It would be particularly desirable to provide a
method and apparatus for rapidly collecting imagery from blood and
other bodily fluids (and cellular compartments), and to provide
software tools for analyzing such imagery to identify cellular
abnormalities or cellular distribution abnormalities associated
with a disease condition.
SUMMARY
This application specifically incorporates by reference the
disclosures and drawings of each patent application and each issued
patent identified above as a related application.
Aspects of the concepts disclosed herein relate to the collection
of multispectral images from a population of cells, and the
analysis of the collected images to measure at least one
characteristic of the population, using photometric and/or
morphometric image features calculated from the collection of
images, where the image feature is associated with a disease
condition. In an exemplary application, the cells are obtained from
bodily fluids and cellular compartments, and in a particularly
preferred implementation, from blood, most preferably where the
cellular compartments are bone marrow and lymph nodes. In a further
particularly preferred implementation, both photometric and
morphometric image features are used in the analysis. In a
particularly preferred, but not limiting implementation, the
plurality of images for each individual object are collected
simultaneously.
Exemplary steps that can be used to analyze biological cells in
accord with an aspect of the concepts disclosed herein includes
collecting image data from a population of cells, and identifying
one or more subpopulations of cells from the image data. In one
implementation, a subpopulation corresponding to cells exhibiting
abnormalities associated with a disease condition is identified.
Such subpopulations can be identified based on empirical evidence
indicating that one or more photometric and/or morphometric image
features are typically associated with the cellular abnormality
associated with the disease condition. The term "image feature" is
intended to refer to a calculated value that quantitatively
characterizes a particular structure, region, visual property,
biochemical abundance, biochemical location, or other aspect of a
cell that can be readily discerned from one or more images of the
cell. The photometric and/or morphometric image features calculated
from the collected images are analyzed to enable at least one
characteristic of a cell or population of cells to be measured.
Cellular characteristics that have been empirically associated with
the cellular abnormalities present during a particular disease
condition (e.g. an increase in expression of a particular cell
surface protein (which can be labeled with a marker) as measured
using a photometric "intensity" image feature or an increase in
cell size as measured using a morphometric "cell area" image
feature) can be detected in the data to determine whether a
particular disease condition is present in the population of cells
originally imaged.
In yet another exemplary implementation, a disease condition may be
detected even when the cells themselves do not exhibit any
abnormalities that can be identified by photometric and/or
morphometric image features. In such an implementation, a sample
will include a plurality of different subpopulations, each of which
is identified by its normal characteristic morphometric and
photometric image features. Where a disease condition is not
present, the ratio of the subpopulations relative to one another
will vary within a determinable range across different patients.
Where a disease condition is present, the disease condition can
alter the ratio of subpopulations, such that a change in the ratio
beyond a normal range can indicate the presence of a disease
condition.
Consider a population of blood cells from a healthy patient. The
ratio of lymphocytes to other types of blood cells can be
determined by analyzing image data of the entire population of
blood cells to classify the images according to blood cell type.
When this same process is applied to a population of blood cells
from a patient with CLL, the ratio of lymphocytes to other types of
blood cells will be significantly different than the ratio
identified in a patient not afflicted with CLL. Thus, a disease
condition can be detected by analyzing a population of cells to
identify subpopulations present in the population, and by
determining changes in the ratios of the subpopulations that
suggest the presence of a disease condition.
In yet another exemplary implementation, a disease condition may be
detected by the presence of an uncharacteristic cell type. In such
an implementation, a sample will include a plurality of different
subpopulations, each of which is identified by its characteristic
morphometric and photometric image features. Where a disease
condition is not present, only the expected subpopulations will be
evident within the sample, though they may vary within a normal
range across different patients. Where a disease condition is
present, an entirely atypical cell type may be evident in the
sample. For example, metastatic cancer of the breast may be
evidenced by the presence of distinctive epithelial cells at some
level in the blood. Thus, a disease condition can be detected by
analyzing a population of cells to identify subpopulations present
in the population, and determining the prevalence of atypical
subpopulations that suggest the presence of a disease condition.
The disease condition may be further refined by analyzing the
morphometric and photometric image features of the atypical cell
population to determine its tissue of origin or metastatic state.
For example, the presence of a large fraction of rapidly dividing
cells, as evidenced by a high nuclear to cellular size ratio image
feature, may characterize a circulating tumor cell as
aggressive.
In still another exemplary implementation, a disease condition may
be detected by the analysis not only of the cell subpopulations and
their relative abundance, but also by an analysis of free (not
cell-associated) bio-molecules within the cell sample. In such an
implementation, a reagent may be added to the cell sample, the
reagent comprising reactive substrates, each of which indicates the
amount of a particular bio-molecule present in the sample. Each
reactive substrate (e.g., a microsphere) includes a unique optical
signature that identifies the species of bio-molecule to which it
preferentially binds, as well as potentially indicating the amount
of that bio-molecule in the sample. By analyzing the imagery of a
co-mingled sample of reactive substrates and cells, the former may
be distinguished from the latter, and both a molecular and cellular
analysis can be performed on the sample in a multiplexed
fashion.
Image data for the population and subpopulation(s) can be
manipulated using several different techniques. An exemplary
technique is referred to as gating, which is a method of
graphically defining a sub-population of cells on a histogram or
scatter plot of photometric or morphometric cell image features for
a given cell population. A further exemplary technique is
backgating, in which a previously-defined sub-population is
graphically highlighted on a histogram or scatter plot of
photometric or morphometric cell image features of a cell
population. While not strictly required, signal processing is
preferably performed on the collected image data to reduce
crosstalk and enhance spatial resolution, particularly for image
data collected using simultaneous multi-channel imaging.
In an exemplary implementation, image data is collected from two
different populations of cells (noting that image data of either of
the two different populations can also be compared to image data of
other cell populations if desired). The image data is analyzed to
identify image features that quantify differences between the two
different cell populations. Many different strategies can be
employed in selecting the two different cell populations. In some
embodiments, the first cell population will include some known
anomaly, and the second population will be known to be normal (or
at least known to correspond to a baseline cell population, where
the anomalous cell population is somehow manipulated or exposed to
some factor, and the baseline cell population has not been
similarly manipulated or exposed), enabling differences between the
two populations to be quantified using the image data. The anomaly
can include, but is not limited to, the presence of neoplastic
cells, the presence of necrotic cells, the presence of cells
exposed to a toxic agent, the presence of cells exposed to a
therapeutic agent, the presence of cells exposed to a stimulating
agent, the presence of cells exposed to a chemical agent, the
presence of cells exposed to a viral agent, the presence of cells
exposed to a bacterial agent, the presence of cells exposed to a
nutrient, the presence of cells exposed to an environmental change,
and the presence of different cells whose relative abundance is
associated with an anomalous condition.
The two different populations of cells can be acquired from
different sources. For example, the anomalous cell population can
be acquired from a person suffering some condition, and the
baseline or normal cell population can be acquired from a healthy
person. In an other example, the anomalous cell population can be
acquired from a prepubescent male or female, and the baseline or
normal cell population can be acquired from a post pubescent male
or female (noting that in this case, it does not matter whether the
prepubescent cells or adult cells are considered to represent the
anomalous or baseline population).
The two different populations of cells can be acquired from the
same source, at the same time, with the anomalous cell population
being manipulated or exposed to some agent, and the baseline or
normal cell population not being similarly manipulated or
exposed.
The two different populations of cells can be acquired from the
same source, at different times, to aid in quantifying cellular
changes over time. In this case, it does not matter whether the
relatively older sample or the relatively newer sample is
considered to represent the anomalous or baseline population. In
addition to simply a passage of time, some other factor may
contribute to some change in the cell populations. The factor can
include a change in diet, a change in stress, a change in
environmental conditions, a change in health, exposure to
environmental factors, exposure to therapeutic agents, exposure to
toxins, exposure to viruses, exposure to infectious agents, and
many other factors.
Yet another aspect of the techniques disclosed herein relates to
monitoring the treatment of a patient exhibiting a disease
condition. Baseline data are collected by imaging a population of
cells from the patient before treatment. For example, the
population of cells can be obtained from a bodily fluid, such as
blood. During the course of treatment, additional data are obtained
by imaging additional populations of cells collected from the
patient during and after various stages of the treatment process.
Such data will provide a quantitative indication of the improved
condition of the patient suffering from the disease condition, as
indicated by either the amount of cells expressing the disease
condition versus normal cells, or by a change in a ratio of the
subpopulations present in the population. Significantly, such
quantification is not feasible with standard microscopy and/or
conventional flow cytometry.
In another exemplary implementation of the techniques disclosed
herein, the imagery collected from a population of biological cells
includes collection of multimodal images. That is, the images
collected will include at least two of the following types of
images: one or more images corresponding to light emitted from the
cell (e.g. a fluorescence image), one or more images corresponding
to light transmitted by the cell (e.g. a bright field image), and
one or more images corresponding to light scattered by the cell
(e.g. a dark field image). Such multimode imaging can encompass any
of the following types of images or combinations thereof: (1) one
or more fluorescence images and at least one bright field image;
(2) one or more fluorescence images and at least one dark field
image; (3) one or more fluorescence images, a bright field image,
and a dark field image; and (4) a bright field image and a dark
field image. Simultaneous collection of a plurality of different
fluorescence images (separated by spectrum) can also be beneficial,
as well as simultaneous collection of a plurality of different
bright field images (for example, using transmitted light with two
different spectral filters). The multimode images can preferably be
collected simultaneously.
As discussed above, image data for a plurality of images of
individual cells that are acquired simultaneously can be used to
detect a disease condition. Note that such an application is based
on identifying and/or quantifying differences between a first cell
population and a second cell population, by analyzing the image
data collected for each cell population. Generally, as described
above, the image data can be analyzed to identify quantifiable
photometric and morphometric differences between the first and
second cell populations. The image data can also be used to
identify a cell type present in one of the first and second cell
populations, but not the other of the first and second cell
populations. Similarly, the image data can also be used to identify
differences in the relative distribution of cell types in the first
and second cell populations, to determine if there is more or less
of a particular cell type in the first population of cells, as
compared to the second population of cells (and vice versa). These
techniques can provide diagnostic information about a patient from
whom the cells are obtained, beyond simply determining if a
specific disease condition exists.
In one exemplary embodiment, the first and second population of
cells are obtained from a person at different times. In another
exemplary embodiment, the first and second population of cells are
obtained from a person at the same time, but then treated
differently before being imaged as described above. For example, a
single blood sample or bodily fluid sample can be acquired from a
person, and that sample can be split into two fractions for
different treatment prior to imaging. Image data for the first
fraction (the first population of cells) can be acquired. The
second fraction (i.e., the second population of cells) can be
exposed to a stimulus before image data are acquired. The image
data from the first and second populations of cells can then be
analyzed to determine how the cell populations have changed.
In one related embodiment, image data from a first population of
cells and a second population of cells are analyzed to determine if
variations in a specific cell type present in both populations
exist, regardless of whether those differences are indicative of a
disease condition. This technique is generally directed at
acquiring the first and second cell populations from a person at
different times, and determining if there are differences between
the same cell type in the first and second populations due to
changes over time. If data are available indicating the conditions
experienced by the person during the time between acquiring the
samples (e.g. a change in medication), then an attempt to correlate
the changes to such conditions can be performed. Even where no such
correlations can be found, any changes identified may be indicative
of changes in the health of the person. For example, some cellular
changes may suggest that the health of the patient has improved or
declined, even if no specific disease condition is identified.
Furthermore, even if no change in the first and second cell
populations is identified, that determination may itself represent
valuable diagnostic data, either indicating that the health of the
person has not appreciably changed, or if the person's health has
changed, indicating that the specific cell type is likely not
related to the change in health.
In another exemplary embodiment, image data from a first population
of cells and a second population of cells are analyzed to determine
if there has been a change in the relative distribution of
different types of cells present in both populations, where such a
change is not limited to being indicative of a specific disease
condition, but may still be relevant to the health of the person
from whom the first and second cell populations were obtained. This
analysis includes determining if a specific cell type is present in
the first cell population, but not the second cell population, and
vice versa, as well as determining how the relative percentages of
cell types present in both the first and second cell populations
has changed. This technique is generally directed at acquiring the
first and second cell populations from a person at different times,
and determining if there are differences between the distribution
of different cell types in the first and second populations. If
data are available indicating the conditions experienced by the
person during the time between acquiring the samples, then an
attempt to correlate the changes to such conditions can be
performed. Even where no such correlations can be found, any
changes identified may be indicative of the health of the person.
For example, some cell signaling molecule distribution changes may
suggest that the health of the patient has improved or declined,
even if no specific disease condition is identified. Furthermore,
even if no change in the cell signaling molecule distributions in
the first and second cell populations is identified, that itself
may be valuable diagnostic data, either indicating that the health
of the person has not appreciably changed, or if the person's
health has changed, indicating that the molecule distribution
analyzed is likely not related to the change in health.
In another exemplary embodiment, image data from a first population
of cells and a second population of cells are analyzed to determine
how the second population of cells responds to a stimulus not
applied to the first population of cells, in order to either detect
a disease condition or to collect information relevant to the
health of the person from whom the populations of cells were
obtained, without specifically identifying a disease condition. In
general, this technique is based on acquiring one sample from a
person, and splitting that sample into two different fractions (the
two different cell populations can be acquired from the person at
different times, however doing so will introduce an additional
variable). The first population of cells acts as a control. A
stimulus is applied to the second population of cells so that the
effect of the stimulus on the cells can be determined by comparing
data collected for the two populations.
This Summary has been provided to introduce a few concepts in a
simplified form that are further described in detail below in the
Description. However, this Summary is not intended to identify key
or essential features of the claimed subject matter, nor is it
intended to be used as an aid in determining the scope of the
claimed subject matter.
DRAWINGS
Various aspects and attendant advantages of one or more exemplary
embodiments and modifications thereto will become more readily
appreciated as the same becomes better understood by reference to
the following detailed description, when taken in conjunction with
the accompanying drawings, wherein:
FIG. 1A is a schematic diagram of an exemplary flow imaging system
that can be used to simultaneously collect a plurality of images
from an object in flow;
FIG. 1B is a plan view of an exemplary flow imaging system that
employs a spectral dispersion component comprising a plurality of
stacked dichroic filters employed to spectrally separate the light
to simultaneously collect a plurality of images from an object in
flow;
FIG. 1C illustrates an exemplary set of images projected onto the
TDI detector when using the spectral dispersing filter system of
the FIG. 1B;
FIG. 2 is a pictorial representation of an image recorded by the
flow imaging system of FIG. 1;
FIG. 3 is a flow chart of the overall method steps implemented in
one aspect of the concepts disclosed herein;
FIG. 4 is an exemplary graphical user interface used to implement
the method steps of FIG. 3;
FIG. 5 is an exemplary graphical user interface used to implement
the method steps of FIG. 3 as applied to the analysis of human
peripheral blood;
FIG. 6 includes images of normal (i.e., healthy) mammary epithelial
cells;
FIG. 7 includes images of mammary carcinoma (i.e., diseased) cells,
illustrating how quantification of data in a fluorescent channel
serves as an image feature for the disease condition;
FIG. 8A is an exemplary graphical user interface used to implement
the method steps of FIG. 3, illustrating a plurality of different
photometric and morphometric descriptors as shown in FIGS. 8B-8M
that can be used to automatically distinguish images of healthy
mammary epithelial cells from images of mammary carcinoma
cells;
FIG. 9 graphically illustrates the separation of cells in human
peripheral blood into a variety of subpopulations based on
photometric properties;
FIG. 10A graphically illustrates a distribution of normal
peripheral blood mononuclear cells (PBMC) based on image data
collected from a population of cells that do not include mammary
carcinoma cells;
FIG. 10B graphically illustrates a distribution of normal PBMC and
mammary carcinoma cells based on image data collected from a
population of cells that includes both cell types, illustrating how
the distribution of mammary carcinoma cells is distinguishable from
the distribution of the normal PBMC cells;
FIG. 11A graphically illustrates a distribution of normal PBMC and
mammary carcinoma cells based on a measured cytoplasmic area
derived from image data collected from a population of cells that
includes both cell types, illustrating how the distribution of
cytoplasmic area of the mammary carcinoma cells is distinguishable
from the distribution of cytoplasmic area of the normal PBMC
cells;
FIG. 11B graphically illustrates a distribution of normal PBMC and
mammary carcinoma cells based on measured scatter frequency derived
from image data collected from a population of cells that includes
both cell types, illustrating how the distribution of the scatter
frequency of the mammary carcinoma cells is distinguishable from
the distribution of the scatter frequency of the normal PBMC
cells;
FIG. 12 shows composite images of cells generated by combining
bright field and fluorescent images of mammary carcinoma cells;
FIG. 13 shows representative images of five different PBMC
populations that can be defined by scatter data derived from image
data of a population of cells;
FIG. 14 schematically illustrates an exemplary computing system
used to implement the method steps of FIG. 3; and
FIG. 15 is a flow chart illustrating exemplary steps for analyzing
two populations of cells, based on images of the cell populations,
in order to identify and/or quantify differences between the cell
populations.
DESCRIPTION
Figures and Disclosed Embodiments are not Limiting
Exemplary embodiments are illustrated in referenced Figures of the
drawings. It is intended that the embodiments and Figures disclosed
herein are to be considered illustrative rather than
restrictive.
Overview
The present disclosure encompasses a method of using flow imaging
systems that can combine the speed, sample handling, and cell
sorting capabilities of flow cytometry with the imagery,
sensitivity, and resolution of multiple forms of microscopy and
full visible/near infrared spectral analysis to collect and analyze
data relating to disease conditions in blood, particularly
detecting and monitoring chronic lymphocytic leukemia.
An aspect of the concepts disclosed herein relates to a system and
method for imaging and analyzing biological cells entrained in a
flow of fluid. In at least one embodiment, a plurality of images of
biological cells are collected simultaneously; the plurality of
images including at least two of the following types of images: a
bright field image, a dark field image, and a fluorescent image.
Images are collected for a population of biological cells. Once the
imagery has been collected, the images can be processed to identify
a subpopulation of images, where the subpopulation shares
photometric and/or morphometric characteristics empirically
determined to be associated with a disease condition.
With respect to the following disclosure, and the claims that
follow, it should be understood that the term "population of cells"
refers to a group of cells including a plurality of objects. Thus,
a population of cells must include more than one cell.
A preferred imaging system to be used in collecting the image data
required to implement the techniques disclosed herein will
incorporate the following principal characteristics:
1. high speed measurement;
2. the ability to process very large or continuous samples;
3. high spectral resolution and bandwidth;
4. good spatial resolution;
5. high sensitivity; and
6. low measurement variation.
In particular, a recently developed imaging flow cytometer
technology, which is embodied in an ImageStream.TM. instrument
(Amnis Corporation, Seattle, Wash.), makes great strides in
achieving each of the above-noted principle characteristics. The
ImageStream.TM. instrument is a commercial embodiment of the flow
imaging systems described below in detail with respect to FIG. 1.
These significant advancements in the art of flow cytometery are
described in the following commonly assigned patents: U.S. Pat. No.
6,249,341, issued on Jun. 19, 2001 and entitled "Imaging And
Analyzing Parameters of Small Moving Objects Such As Cells;" U.S.
Pat. No. 6,211,955 issued on Apr. 3, 2001, also entitled "Imaging
And Analyzing Parameters of Small Moving Objects Such As Cells;"
U.S. Pat. No. 6,473,176, issued on Oct. 29, 2002, also entitled
"Imaging And Analyzing Parameters of Small Moving Objects Such As
Cells;" U.S. Pat. No. 6,583,865, issued on Jun. 24, 2003, entitled
"Alternative Detector Configuration And Mode of Operation of A Time
Delay Integration Particle Analyzer;" U.S. patent application Ser.
No. 09/989,031 entitled "Imaging And Analyzing Parameters of Small
Moving Objects Such As Cells in Broad Flat Flow." While the
ImageStream.TM. platform represents a particularly preferred
imaging instrument used to acquire the image data that will be
processed in accord with the concepts disclosed herein, it should
be understood that the concepts disclosed herein are not limited
only to the use of that specific instrument.
As noted above, in addition to collecting image data from a
population of biological cells, an aspect of the concepts disclosed
herein involves processing the image data collected to measure at
least one characteristic associated with a disease condition in the
imaged population. A preferred image analysis software package is
IDEAS.TM. (Amnis Corporation, Seattle, Wash.). The IDEAS.TM.
package evaluates nearly 200 image features for every cell,
including multiple morphologic and fluorescence intensity
measurements, which can be used to define and characterize cell
populations. The IDEAS.TM. package enables the user to define
biologically relevant cell subpopulations, and analyze
subpopulations using standard cytometry analyses, such as gating
and backgating. It should be understood, however, that other image
analysis methods or software packages can be implemented to apply
the concepts disclosed herein, and the preferred image analysis
software package that is disclosed is intended to be exemplary,
rather than limiting of the concepts disclosed herein.
Overview of a Preferred Imaging System
FIG. 1 is a schematic diagram of a preferred flow imaging system
510 (functionally descriptive of the ImageStream.TM. platform) that
uses TDI when capturing images of objects 502 (such as biological
cells), entrained in a fluid flow 504. System 510 includes a
velocity detecting subsystem that is used to synchronize a TDI
imaging detector 508 with the flow of fluid through the system.
Significantly, imaging system 510 is capable of simultaneously
collecting a plurality of images of an object. A particularly
preferred implementation of imaging system 510 is configured for
multi-spectral imaging and can operate with six spectral channels:
DAPI fluorescence (400-460 nm), Dark field (460-500 nm), FITC
fluorescence (500-560 nm), PE fluorescence (560-595 nm), Bright
field (595-650 nm), and Deep Red (650-700 nm). The TDI detector can
provide 10 bit digital resolution per pixel. The numeric aperture
of the preferred imaging system is typically 0.75, with a pixel
size of approximately 0.5 microns. However, those skilled in the
art will recognize that this flow imaging system is neither limited
to six spectral channels, nor limited to either the stated aperture
size or pixel size and resolution.
Moving objects 502 are illuminated using a light source 506. The
light source may be a laser, a light emitting diode, a filament
lamp, a gas discharge arc lamp, or other suitable light emitting
source, and the system may include optical conditioning elements
such as lenses, apertures, and filters that are employed to deliver
broadband or one or more desired wavelengths or wavebands of light
to the object with an intensity required for detection of the
velocity and one or more other characteristics of the object. Light
from the object is split into two light paths by a beam splitter
503. Light traveling along one of the light paths is directed to
the velocity detector subsystem, and light traveling along the
other light path is directed to TDI imaging detector 508. A
plurality of lenses 507 are used to direct light along the paths in
a desired direction, and to focus the light. Although not shown, a
filter or a set of filters can be included to deliver to the
velocity detection subsystem and/or TDI imaging detector 508, only
a narrow band of wavelengths of the light corresponding to, for
example, the wavelengths emitted by fluorescent or phosphorescent
molecules in/on the object, or light having the wavelength(s)
provided by the light source 506, so that light from undesired
sources is substantially eliminated.
The velocity detector subsystem includes an optical grating 505a
that amplitude modulates light from the object, a light sensitive
detector 505b (such as a photomultiplier tube or a solid-state
photodetector), a signal conditioning unit 505c, a velocity
computation unit 505d, and a timing control unit 505e, which
assures that TDI imaging detector 508 is synchronized to the flow
of fluid 504 through the system. The optical grating preferably
comprises a plurality of alternating transparent and opaque bars
that modulate the light received from the object, producing
modulated light having a frequency of modulation that corresponds
to the velocity of the object from which the light was received.
Preferably, the optical magnification and the ruling pitch of the
optical grating are chosen such that the widths of the bars are
approximately the size of the objects being illuminated. Thus, the
light collected from cells or other objects is alternately blocked
and transmitted through the ruling of the optical grating as the
object traverses the interrogation region, i.e., the field of view.
The modulated light is directed toward a light sensitive detector,
producing a signal that can be analyzed by a processor to determine
the velocity of the object. The velocity measurement subsystem is
used to provide timing signals to TDI imaging detector 508.
Preferably, signal conditioning unit 505c comprises a programmable
computing device, although an ASIC chip or a digital oscilloscope
can also be used for this purpose. The frequency of the
photodetector signal is measured, and the velocity of the object is
computed as a function of that frequency. The velocity dependent
signal is periodically delivered to a TDI detector timing control
505e to adjust the clock rate of TDI imaging detector 508. Those of
ordinary skill in the art will recognize that the TDI detector
clock rate is adjusted to match the velocity of the image of the
object over the TDI detector to within a small tolerance selected
to ensure that longitudinal image smearing in the output signal of
the TDI detector is within acceptable limits. The velocity update
rate must occur frequently enough to keep the clock frequency
within the tolerance band as flow (object) velocity varies.
Beam splitter 503 has been employed to divert a portion of light
from an object 502 to light sensitive detector 505b, and a portion
of light from object 502a to TDI imaging detector 508. In the light
path directed toward TDI imaging detector 508, there is a plurality
of stacked dichroic filters 509, which separate light from object
502a into a plurality of wavelengths. One of lenses 507 is used to
form an image of object 502a on TDI imaging detector 508.
The theory of operation of a TDI detector like that employed in
system 510 is as follows. As objects travel through a flow tube 511
(FIG. 1) and pass through the volume imaged by the TDI detector,
light from the objects forms images of the objects, and these
images travel across the face of the TDI detector. The TDI detector
preferably comprises a charge coupled device (CCD) array, which is
specially designed to allow charge to be transferred on each clock
cycle, in a row-by-row format, so that a given line of charge
remains locked to, or synchronized with, a line in the image. The
row of charge is clocked out of the array and into a memory when it
reaches the bottom of the array. The intensity of each line of the
signal produced by the TDI detector corresponding to an image of an
object is integrated over time as the image and corresponding
resulting signal propagate over the CCD array. This technique
greatly improves the signal-to-noise ratio of the TDI detector
compared to non-integrating type detectors--a feature of great
benefit in a detector intended to respond to images from low-level
fluorescence emission of an object. Proper operation of the TDI
detector requires that the charge signal be clocked across the CCD
array in synchronization with the rate at which the image of the
object moves across the CCD array. An accurate clock signal to
facilitate this synchronization can be provided by determining the
velocity of the object, and the concepts disclosed herein use an
accurate estimate of the object's velocity, and thus, of the
velocity of the image as it moves over the CCD array of the TDI
detector. A flow imaging system of this type is disclosed in
commonly assigned U.S. Pat. No. 6,249,341, the complete disclosure,
specification, and drawings of which are hereby specifically
incorporated herein by reference.
FIG. 2 is a pictorial representation of images produced by the flow
imaging system of FIG. 1. A column 520, labeled "BF," includes
images created by the absorption of light from light source 506 by
spherical objects 502 entrained in fluid flow 504. The "BF" label
refers to "bright field," a term derived from a method for creating
contrast in an image whereby light is passed through a region and
the absorption of light by objects in the region produces dark
areas in the image. The background field is thus bright, while the
objects are dark in this image. Thus, column 520 is the "bright
field channel." It should be understood that the inclusion of a
bright field image is exemplary, rather than limiting on the scope
of the concepts disclosed herein. Preferably, the concepts
disclosed herein utilize a combination of bright field images and
fluorescent images, or of dark field images and fluorescent
images.
The remaining three columns 522, 524, and 526 shown in FIG. 2 are
respectively labeled ".lamda.1," ".lamda.2," and ".lamda.3." These
columns include images produced using light that has been emitted
by an object entrained in the fluid flow. Preferably, such light is
emitted through the process of fluorescence (as opposed to images
produced using transmitted light). As those of ordinary skill in
the art will recognize, fluorescence is the emission of light (or
other electromagnetic radiation) by a substance that has been
stimulated by the absorption of incident radiation. Generally,
fluorescence persists only for as long as the stimulating radiation
persists. Many substances (particularly fluorescent dyes) can be
identified based on the spectrum of the light that is produced when
they fluoresce. Columns 522, 524, and 526 are thus referred to as
"fluorescence channels."
Additional exemplary flow imaging systems are disclosed in commonly
assigned U.S. Pat. No. 6,211,955 and U.S. Pat. No. 6,608,682, the
complete disclosure, specification, and drawings of which are
hereby specifically incorporated herein by reference as background
material. The imaging systems described above and in these two
patents in detail, and incorporated herein by reference, have
substantial advantages over more conventional systems employed for
the acquisition of images of biological cell populations. These
advantages arise from the use in several of the imaging systems of
an optical dispersion system, in combination with a TDI detector
that produces an output signal in response to the images of cells
and other objects that are directed onto the TDI detector.
Significantly, multiple images of a single object can be collected
at one time. The image of each object can be spectrally decomposed
to discriminate object characteristics by absorption, scatter,
reflection, or emissions, using a common TDI detector for the
analysis. Other systems include a plurality of detectors, each
dedicated to a single spectral channel.
These imaging systems can be employed to determine morphological,
photometric, and spectral characteristics of cells and other
objects by measuring optical signals including light scatter,
reflection, absorption, fluorescence, phosphorescence,
luminescence, etc. Morphological parameters include area,
perimeter, texture or spatial frequency content, centroid position,
shape (i.e., round, elliptical, barbell-shaped, etc.), volume, and
ratios of selected pairs (or subsets) of these parameters. Similar
parameters can also be determined for the nuclei, cytoplasm, or
other sub-compartments of cells with the concepts disclosed herein.
Photometric measurements with the preferred imaging system enable
the determination of nuclear optical density, cytoplasm optical
density, background optical density, and ratios of selected pairs
of these values. An object being imaged with the concepts disclosed
herein can either be stimulated into fluorescence or
phosphorescence to emit light, or may be luminescent, producing
light without stimulation. In each case, the light from the object
is imaged on the TDI detector to use the concepts disclosed herein
to determine the presence and amplitude of the emitted light, the
number of discrete positions in a cell or other object from which
the light signal(s) originate(s), the relative placement of the
signal sources, and the color (wavelength or waveband) of the light
emitted at each position in the object.
Using a Multispectral Imaging System to Analyze Bodily Fluid for a
Disease Condition
As noted above, aspects of the concepts disclosed herein involve
both the collection of multispectral images from a population of
biological cells, and the analysis of the collected images to
identify at least one photometric or morphological image feature
that has been empirically determined to be associated with a
disease condition. Thus, an aspect of the present disclosure
relates to the use of both photometric and morphometric image
features derived from multi-mode imagery of objects (e.g., cells)
in flow to discriminate cell characteristics in populations of
cells, to facilitate the detection of the presence of a disease
condition. Discussed in more detail below are methods for analyzing
cells in suspension or flow, which may be combined with
comprehensive multispectral imaging to provide morphometric and
photometric data to enable, for example, the quantization of
characteristics exhibited by both normal cells and diseased cells,
to facilitate the detection of diseased or abnormal cells
indicative of a disease condition. Heretofore, such methods have
not been feasible with standard microscopy and/or flow
cytometry.
As noted above, a preferred flow imaging system (e.g., the
ImageStream.TM. platform) can be used to simultaneously acquire
multispectral images of cells in flow, to collect image data
corresponding to bright field, dark field, and four channels of
fluorescence. The ImageStream.TM. platform is a commercial
embodiment based on the imaging systems described in detail above.
In general, cells are hydrodynamically focused into a core stream
and orthogonally illuminated for both dark field and fluorescence
imaging. The cells are simultaneously trans-illuminated via a
spectrally-limited source (e.g., filtered white light or a light
emitting diode) for bright field imaging. Light is collected from
the cells with an imaging objective lens and is projected on a CCD
array. The optical system has a numeric aperture of 0.75 and the
CCD pixel size in object space is 0.5.mu..sup.2, enabling high
resolution imaging at event rates of approximately 100 cells per
second. Each pixel is digitized with 10 bits of intensity
resolution in this example, providing a minimum dynamic range of
three decades per pixel. In practice, the spread of signals over
multiple pixels results in an effective dynamic range that
typically exceeds four decades per image. Additionally, the
sensitivity of the CCD can be independently controlled for each
multispectral image, resulting in a total of approximately six
decades of dynamic range across all the images associated with an
object. It should be understood that while the ImageStream.TM.
platform represents a particularly preferred flow imaging system
for acquiring image data in accord with the concepts disclosed
herein, the ImageStream.TM. platform is intended to represent an
exemplary imaging system, rather than limiting the concepts
disclosed. Any imaging instrument capable of collecting images of a
population of biological cells sufficient to enable the image
analysis described in greater detail below to be achieved can be
implemented in accord with the concepts presented herein.
Referring again to the preferred imaging system, the
ImageStream.TM. platform, prior to projection on the CCD, the light
is passed through a spectral decomposition optical system that
directs different spectral bands to different lateral positions
across the detector (such spectral decomposition is discussed in
detail above in connection with the description of the various
preferred embodiments of imaging systems). With this technique, an
image is optically decomposed into a set of a plurality of
sub-images (preferably 6 sub-images, including: bright field, dark
field, and four different fluorescent images), each sub-image
corresponding to a different spectral (i.e., color) component and
spatially isolated from the remaining sub-images. This process
facilitates identification and quantization of signals within the
cell by physically separating on the detector signals that may
originate from overlapping regions of the cell. Spectral
decomposition also enables multimode imaging, i.e., the
simultaneous detection of bright field, dark field, and multiple
colors of fluorescence. The process of spectral decomposition
occurs during the image formation process, rather than via digital
image processing of a conventional composite image.
The CCD may be operated using TDI to preserve sensitivity and image
quality even with fast relative movement between the detector and
the objects being imaged. As with any CCD, image photons are
converted to photo charges in an array of pixels. However, in TDI
operation, the photo charges are continuously shifted from pixel to
pixel down the detector, parallel to the axis of flow. If the photo
charge shift rate is synchronized with the velocity of the image of
the cell, the effect is similar to physically panning a camera.
Image streaking is avoided despite signal integration times that
are orders of magnitude longer than in conventional flow cytometry.
For example, an instrument may operate at a continuous data rate of
approximately 30 mega pixels per second and integrate signals from
each object for 10 milliseconds, enabling the detection of even
faint fluorescent probes within cell images to be acquired at
relatively high speed. Careful attention to pump and fluidic system
design to achieve highly laminar, non-pulsatile flow eliminates any
cell rotation or lateral translation on the time scale of the
imaging process (see, e.g., U.S. Pat. No. 6,532,061).
A real-time algorithm analyzes every pixel read from the CCD to
detect the presence of object images and calculate a number of
basic morphometric and photometric image features, which can be
used as criteria for data storage. Data files encompassing
10,000-20,000 cells are typically about 100 MB in size and,
therefore, can be stored and analyzed using standard personal
computers. The TDI readout process operates continuously without
any "dead time," which means every cell can be imaged and the
coincidental imaging of two or more cells at a time either in
contact or not, presents no barrier to data acquisition.
Such an imaging system can be employed to determine morphological,
photometric, and spectral characteristics of cells and other
objects by measuring optical signals, including light scatter,
reflection, absorption, fluorescence, phosphorescence,
luminescence, etc. As used herein, morphological parameters (i.e.,
morphometrics) may be basic (e.g., nuclear shape) or may be complex
(e.g., identifying cytoplasm size as the difference between cell
size and nuclear size). For example, morphological parameters may
include nuclear area, perimeter, texture or spatial frequency
content, centroid position, shape (i.e., round, elliptical,
barbell-shaped, etc.), volume, and ratios of selected pairs of
these parameters. Morphological parameters of cells may also
include cytoplasm size, texture or spatial frequency content,
volume, and the like. As used herein, photometric measurements with
the aforementioned imaging system can enable the determination of
nuclear optical density, cytoplasm optical density, background
optical density, and the ratios of selected pairs of these values.
An object being imaged can be stimulated into fluorescence or
phosphorescence to emit light, or may be luminescent, wherein light
is produced by the object without stimulation. In each case, the
light from the object may be imaged on a TDI detector of the
imaging system to determine the presence and amplitude of the
emitted light, the number of discrete positions in a cell or other
object from which the light signal(s) originate(s), the relative
placement of the signal sources, and the color (wavelength or
waveband) of the light emitted at each position in the object.
The present disclosure provides methods of using both photometric
and morphometric image features derived from multi-mode imagery of
objects in flow. Such methods can be employed as a cell analyzer to
determine if one or more image features corresponding to a disease
condition is present in the population of cells imaged. As noted
above, certain image features can be indicative of the cellular
abnormality associated with a disease condition, or image features
can be indicative of a change in a ratio of subpopulations present
in the population of the cells imaged, where the change in ratio is
indicative of a disease condition. Preferably the population of
cells is imaged while entrained in a fluid flowing through an
imaging system. As used herein, gating refers to a subset of data
relating to photometric or morphometric imaging. For example, a
gate may be a numerical or graphical boundary of a subset of data
that can be used to define the characteristics of particles to be
further analyzed. Here, gates have been defined, for example, as a
plot boundary that encompasses "in focus" cells, or sperm cells
with tails, or sperm cells without tails, or cells other than sperm
cells, or sperm cell aggregates, or cell debris. Further,
backgating may be a subset of the subset data. For example, a
forward scatter versus a side scatter plot in combination with a
histogram from an additional image feature may be used to backgate
a subset of cells within the initial subset of cells.
In using an imaging system as described herein, it should be made
clear that a separate light source is not required to produce an
image of the object (cell), if the object is luminescent (i.e., if
the object produces light). However, many of the applications of an
imaging system as described herein will require that one or more
light sources be used to provide light that is incident on the
object being imaged. A person having ordinary skill in the art will
know that the locations of the light sources substantially affect
the interaction of the incident light with the object and the kind
of information that can be obtained from the images using a
detector.
In addition to imaging an object with the light that is incident on
it, a light source can also be used to stimulate emission of light
from the object. For example, a cell having been contacted with a
probe conjugated to a fluorochrome (e.g., such as FITC, PE, APC,
Cy3, Cy5, or Cy5.5) will fluoresce when excited by light, producing
a corresponding characteristic emission spectra from any excited
fluorochrome probe that can be imaged on a TDI detector. Light
sources may alternatively be used for causing the excitation of
fluorochrome probes on an object, enabling a TDI detector to image
fluorescent spots produced by the probes on the TDI detector at
different locations as a result of the spectral dispersion of the
light from the object that is provided by a prism. The disposition
of these fluorescent spots on the TDI detector surface will depend
upon their emission spectra and their location in the object.
Each light source may produce light that can either be coherent,
non-coherent, broadband, or narrowband light, depending upon the
application of the imaging system desired. Thus, a tungsten
filament light source can be used for applications in which a
narrowband light source is not required. For applications such as
stimulating the emission of fluorescence from probes, narrowband
laser light is preferred, since it also enables a spectrally
decomposed, non-distorted image of the object to be produced from
light scattered by the object. This scattered light image will be
separately resolved from the fluorescent spots produced on a TDI
detector, so long as the emission spectra of any of the spots are
at different wavelengths than the wavelength of the laser light.
The light source can be either of the continuous wave (CW) or
pulsed type, such as a pulsed laser. If a pulsed type illumination
source is employed, the extended integration period associated with
TDI detection can enable the integration of signals from multiple
pulses. Furthermore, it is not necessary for the light to be pulsed
in synchronization with the TDI detector.
Particularly for use in collecting image data for cell populations
found in bodily fluids such as blood, it can be desirable to employ
a 360 nm UV laser as a light source, and to optimize the optical
system of the imaging system for diffraction-limited imaging
performance in the 400-460 nm (DAPI emission) spectral band.
In embodiments consistent with the disclosure herein, it is to be
understood that relative movement exists between the object being
imaged and the imaging system. In most cases, it will be more
convenient to move the object than to move the imaging system. It
is also contemplated that in some cases, the object may remain
stationary and the imaging system move relative to it. As a further
alternative, both the imaging system and the object may be in
motion, which movement may be in different directions and/or at
different rates.
Exemplary Imaging System and Detector
While the principles of preferred imaging systems have been
discussed above, the following provides a more detailed description
of an exemplary imaging system, and an exemplary detector, in order
to describe how the imaging optics and detector cooperate to
achieve the simultaneous collection of a plurality of images.
The following imaging system employs a spectral dispersion filter
assembly that does not convolve the acquired images with the
emission spectra of the light forming the images, thereby
eliminating the need for deconvolution of the emission spectra from
the image. FIG. 1B illustrates a non-distorting spectral dispersion
system 250 that employs a five color stacked wedge spectral
dispersing filter assembly 252.
In FIG. 1B (which is a plan view), a fluid flow 22 entrains an
object 24 (such as a cell, but alternatively, a small particle) and
carries the object through the imaging system. The direction of the
fluid flow in FIG. 1B is into (or out of) the drawing sheet. Light
30 from object 24 passes through collection lenses 32a and 32b that
collect the light, producing collected light 253, which is
approximately focused at infinity, i.e. the rays of collected light
from collection lens 32b are generally parallel. Collected light
253 enters spectral dispersing filter assembly 252, which disperses
the light, producing dispersed light 257. The dispersed light then
enters imaging lenses 40a and 40b, which focuses light 257 onto a
TDI detector 44.
The spectral dispersing filter assembly splits the light into a
plurality of light beams having different bandwidths. Each light
beam thus produced is directed at a different nominal angle so as
to fall upon a different region of TDI detector 44. The nominal
angular separation between each bandwidth produced by the spectral
dispersing filter assembly 252 exceeds the field angle of the
imaging system in object space thereby preventing overlap of the
field images of various bandwidths on the detector.
Spectral dispersing filter assembly 252 comprises a plurality of
stacked dichroic wedge filters, including a red dichroic filter R,
an orange dichroic filter O, a yellow dichroic filter Y, a green
dichroic filter G, and a blue dichroic filter B. Red dichroic
filter R is placed in the path of collected light 34, oriented at
an angle of approximately 44.0.degree. relative to an optic axis
253 of collection lenses 32a and 32b. Light of red wavelengths and
above, i.e., >640 nm, is reflected from the surface of red
dichroic filter R at a nominal angle of 1.degree., measured
counter-clockwise from a vertical optic axis 257. The light
reflected by red dichroic filter R leaves spectral dispersing
filter assembly 252 and passes through imaging lenses 40a and 40b,
which cause the light to be imaged onto a red light receiving
region of TDI detector 44, which is disposed toward the right end
of the TDI detector, as shown in FIG. 1B.
Orange dichroic filter O is disposed a short distance behind red
dichroic filter R and is oriented at an angle of 44.5 degrees with
respect to optic axis 253. Light of orange wavelengths and greater,
i.e., >610 nm, is reflected by orange dichroic filter O at a
nominal angle of 0.5.degree. with respect to vertical optic axis
257. Because the portion of collected light 34 comprising
wavelengths longer than 640 nm was already reflected by red
dichroic filter R, the light reflected from the surface of orange
dichroic filter O is effectively bandpassed in the orange colored
region between 610 nm and 640 nm. This light travels at a nominal
angle of 0.5.degree. from vertical optic axis 257, and is imaged by
imaging lenses 40a and 40b so as to fall onto an orange light
receiving region disposed toward the right hand side of TDI
detector 44 between a center region of the TDI detector and the red
light receiving region, again as shown in FIG. 1B.
Yellow dichroic filter Y is disposed a short distance behind orange
dichroic filter O and is oriented at an angle of 45.degree. with
respect to optic axis 253. Light of yellow wavelengths, i.e., 560
nm and longer, is reflected from yellow dichroic filter Y at a
nominal angle of 0.0.degree. with respect to vertical optic axis
257. Wavelengths of light reflected by yellow dichroic filter Y are
effectively bandpassed in the yellow region between 560 nm and 610
nm and are imaged by imaging lenses 40a and 40b near vertical optic
axis 257 so as to fall on a yellow light receiving region toward
the center of TDI detector 44.
In a manner similar to dichroic filters R, O, and Y, dichroic
filters G and B are configured and oriented so as to image green
and blue light wavebands onto respective green and blue light
receiving regions of TDI detector 44, which are disposed toward the
left-hand side of the TDI detector. By stacking the dichroic
filters at different predefined angles, spectral dispersing filter
assembly 252 collectively works to focus light within predefined
wavebands of the light spectrum onto predefined regions of TDI
detector 44.
The wedge shape of the dichroic filters in the preceding discussion
allows the filters to be placed in near contact, in contact or
possibly cemented together to form the spectral dispersing filter
assembly 252. The angle of the wedge shape fabricated into the
substrate for the dichroic filter allows easy assembly of the
spectral dispersing filter assembly 252, forming a monolithic
structure in which the wedge-shaped substrate is sandwiched between
adjacent dichroic filters. If the filters are in contact with each
other or cemented together, the composition of the materials that
determine the spectral performance of the filter may be different
from those which are not in contact. Those of ordinary skill in the
art will appreciate that flat, non wedge-shaped substrates could be
used to fabricate the spectral dispersing filter assembly 252. In
this case another means such as mechanically mounting the filters
could be used to maintain the angular relationships between the
filters.
In addition to the foregoing configuration, non-distorting spectral
dispersion system 250 may optionally include a detector filter
assembly 254 to further attenuate undesired signals in each of the
light beams, depending upon the amount of rejection required for
out-of-band signals. In the embodiment shown in FIG. 1B, light may
pass through each dichroic filter in the spectral dispersing filter
assembly 252 twice before exiting the spectral dispersing filter
assembly 252. This condition will further attenuate out-of-band
signals, but will also attenuate in-band signals.
The foregoing description illustrates the use of a five color
system. Those skilled in the art will appreciate that a spectral
dispersing component with more or fewer filters may be used in
these configurations in order to construct a system covering a
wider or a narrower spectral region, or different passbands within
a given spectral region. Likewise, those skilled in the art will
appreciate that the spectral resolution of the present invention
may be increased or decreased by appropriately choosing the number
and spectral characteristics of the dichroic and or bandpass
filters that are used. Furthermore, those skilled in the art will
appreciate that the angles or orientation of the filters may be
adjusted to direct light of a given bandwidth onto any desired
point on the TDI detector. In addition, there is no need to focus
the light in increasing or decreasing order by wavelength. For
example, in fluorescence imaging applications, one may wish to
create more spatial separation on the TDI detector between the
excitation and emission wavelengths by changing the angles at which
the filters corresponding to those wavelengths are oriented with
respect to the optic axes of the system. Finally, it will be clear
to those skilled in the art that dispersion of the collected light
may be performed on the basis of non-spectral characteristics,
including angle, position, polarization, phase, or other optical
properties.
FIG. 1C illustrates the distribution of images on TDI detector 44
corresponding to imaging a plurality of cells 280-284 using
non-distorting spectral dispersion system 250. Significantly, the
field angle of system 250 is orthogonal to flow in object space,
such that the individual images are laterally dispersed across
detector 44 (as indicated on FIG. 1C), substantially orthogonal to
a direction of a motion of the respective images across the TDI
detector (i.e., the object moves vertically across the detector,
and the plurality of images are dispersed horizontally across the
detector).
In this particular configuration, the field angle in object space
is less than +/-0.25.degree.. Those skilled in the art will
appreciate that the field angle can be made larger or smaller. To
the extent that the field angle is made larger, for example, to
image cells over a wider region on a slide or in a broad flat flow,
the field angle at the detector will increase in proportion to the
number of colors used. FIG. 1C illustrates the image projected onto
the detector when three cells 280, 282 and 284 are flowing through
the field of view. Light scatter images of cells 280, 282, and 284
are seen on the left hand side of the detector denoted as the BLUE
area. Images of cell nuclei 202 stained with a green fluorescent
dye are seen in the GREEN area of the detector. Three
differently-colored genetic probes 204, 205, and 206 are also
employed for the analysis of the sex chromosomes within the cells.
Probe 204 stains the X chromosome with an orange fluorescing dye,
probe 205 stains the Y chromosome with yellow fluorescing dye, and
probe 206 stains the inactive X chromosome in female cells with a
red fluorescing dye. Cell 282 is imaged onto the detector as shown
in FIG. 1C. An image 286 of probe 204 from cell 282 is seen in the
ORANGE area of the detector. Likewise an image 288 of probe 205
from cell 282 is seen in the YELLOW area of the detector. The
signal on the detector is processed to determine the existence and
position of these images on the detector to determine that cell 282
is a male cell. In a similar manner, cells 280 and 284 contain
probes 204 and 206, which create images 290 and 292 in the ORANGE
area of the detector, and images 294 and 296 in the RED area of the
detector, indicating that these cells are female, respectively.
Exemplary High Level Method Steps
FIG. 3 is a flow chart 400 schematically illustrating exemplary
steps that can be used to analyze a population of cells based on
images of the cell population, in order to identify a disease
condition. In a particularly preferred embodiment, the cell
population is obtained from a bodily fluid, such as blood. In a
block 402, an imaging system, such as the exemplary imaging system
described above in detail, is used to collect image data from a
first population of biological cells where a disease condition is
known to be present. In a block 404 at least one photometric or
morphometric image feature associated with the disease condition is
identified. In the empirical study described below, two distinctly
different types of image features were developed. One type of image
feature relates to identifying a photometric and/or morphometric
difference between healthy cells and diseased cells. One technique
in identifying such an image feature is to label carcinoma cells
with a fluorescent label, and compare images of fluorescently
labeled carcinoma cells with images of healthy cells, to identify a
plurality of photometric and morphometric image features associated
with the carcinoma cells. As will be described in greater detail
below, such image features include differences in the average
nucleus size between healthy cells and carcinoma cells, and
differences in fluorescent images of healthy cells and carcinoma
cells. These differences can be quantified based on processing the
image data for the population of cells, to identify images that are
more likely to be images of carcinoma cells, and to identify images
that are more likely to be images of healthy cells.
Another type of image feature relates to identifying some
difference between subpopulations present in a cellular population
absent the disease condition, and subpopulations present in a
cellular population during the disease condition. For example, CLL
is a disease condition where the number of lymphocytes in blood
increases relative to the numbers of other blood cell types. Thus,
a change in the ratio of lymphocytes to other blood cell types can
be indicative of a disease condition.
Once a photometric and/or morphometric image feature associated
with the disease condition is identified, image data are collected
from a second population of cells, in which it is not known whether
the disease condition exists or not. In a block 408 image data are
collected for the second population of cells, and then the image
data are analyzed for the presence of the previously identified
image feature, to determine whether the disease condition is
present in the second population of cells.
Significantly, where the imaging systems described above are used
to collect the image data from a population of cells, the image
data can be collected quite rapidly. In general, the analysis
(i.e., analyzing the collected image data to either initially
identify an image feature or to determine the presence of a
previously identified image feature in a population of cells) will
be performed off-line, i.e., after the collection of the image
data. Current implementations of imaging processing software are
capable of analyzing a relatively large population of cells (i.e.,
tens of thousands of cells) within tens of minutes using readily
available personal computers. However, it should be recognized that
as more powerful computing systems are developed and become readily
available, it may become possible to analyze the image data in
real-time. Thus, off-line processing of the image data is intended
to be exemplary, rather than limiting, and it is contemplated that
real-time processing of the image data is an alternative.
Where the image feature relates to some photometric and/or
morphometric difference between a healthy cell and a diseased cell,
before using an imaging instrument to collect image data on the
first population of cells (the population known to be associated
with the disease condition), it can be desirable to label either
the diseased cells or the healthy cells, particularly where the
first population includes a mixture of both diseased and healthy
cells. This approach facilitates separating the collected image
data into images corresponding to diseased cells and images
corresponding to healthy cells, to facilitate identification of
photometric and/or morphometric image features that can be used to
distinguish the two. It should be recognized however, that the
first population could include only diseased cells, and that if the
image data of the first population is compared with image data of a
cell population known to include only healthy cells, the
photometric and/or morphometric image features that can be used to
distinguish the diseased cells from the healthy cells can readily
be identified.
Where the image feature relates to some photometric and/or
morphometric difference between subpopulations present in a
cellular population absent the disease condition, and
subpopulations present in a cellular population associated with
disease condition, image data corresponding to the subpopulations
present in a healthy cellular population must be provided before
the image data corresponding to the first population of cells (the
population known to be associated with the disease condition) can
be analyzed to identify some photometric and/or morphometric
difference between the subpopulations present in the healthy
cellular population, and the subpopulations present in the cellular
population having the disease condition.
While not strictly required, in a working embodiment of the
techniques described herein, additional processing was implemented
to reduce crosstalk and spatial resolution for the multi-channel
imaging. The crosstalk reduction processing implemented is
described in commonly assigned U.S. Pat. No. 6,763,149, the
specification, disclosure and the drawings of which are hereby
specifically incorporated herein by reference as background
material. Those of ordinary skill in the art will recognize that
other types of crosstalk reduction techniques could alternatively
be implemented.
Identification of Exemplary Photometric and Morphometric Disease
Condition Features
In the context of the present disclosure, the multi-spectral
imaging flow cytometer described above employs UV excitation
capabilities and algorithms to quantitate DNA content and nuclear
morphology, for the purpose of detecting and monitoring disease
conditions, such as chronic lymphocytic leukemia. In addition to
employing a flow imaging instrument including a 360 nm UV laser and
an optical system optimized for diffraction-limited imaging
performance in the 400-460 nm (DAPI emission) spectral band, an
imaging processing system is employed to process the image data. A
personal computer executing image processing software represents an
exemplary imaging processing system. The imaging processing
software incorporates algorithms enabling photometric and/or
morphometric properties of cells to be determined based on images
of the cells. Exemplary algorithms include masking algorithms,
algorithms that define nuclear morphology, algorithms for the
quantization of cell cycle histograms, algorithms for analyzing DNA
content, algorithms for analyzing heterochromaticity, algorithms
for analyzing N/C ratio, algorithms for analyzing granularity,
algorithms for analyzing CD45 expression, and algorithms for
analyzing other parameters. In addition, the imaging processing
software incorporates an algorithm referred to as a classifier, a
software based analysis tool that is configured to evaluate a
sample population of cells to determine if any disease condition
image features are present. For determining the presence of cancer
cells, the classifier will analyze the images of the sample
population for images having photometric and/or morphometric
properties corresponding to previously identified photometric
and/or morphometric properties associated with cancer cells.
For samples of cell populations being analyzed to detect CLL, the
classifier will analyze the images of the sample population to
separate the images into different cellular subpopulations (based
on different types of blood cells), and determine if the ratios of
the subpopulations indicates the presence of CLL (for example,
because of a higher than normal amount of lymphocytes). Preferably,
the classifier configured to detect CLL will separate blood cells
into the following subpopulations: lymphocytes, monocytes,
basophils, neutrophils, and eosinophils. The classifier configured
to detect CLL will be based on empirical data from healthy patients
and from patients with CLL. Classifier profiles for CLL can be
improved by collecting and comparing classifier data for a variety
of patients with the same diagnosis. Preferably, large (10,000 to
20,000-cell) data sets from each patient will be collected to
assess the existence and diagnostic significance of CLL cell
subpopulations for classifier optimization. Such an optimized
classifier can then be used to monitor patient treatment response
and assess residual disease after treatment.
Significantly, for detection of epithelial cell carcinomas, high
rates of data acquisition is required. Such cells have been
reported to range from 1 cell in 100,000 peripheral blood
leukocytes to 1 cell in 1,000,000 peripheral blood leukocytes. The
ImageStream.TM. cytometer and IDEAS.TM. analytical software package
discussed above are ideally suited for this application. Imagery
from peripheral blood leukocytes can be obtained in the absence of
artifacts typical of preparing blood films. Large cell numbers (in
the tens and hundreds of thousands) can be accumulated per sample,
providing greater confidence in the analysis of subpopulations.
Immunofluorescent staining with accepted markers (CD5, CDI9, etc.)
can easily be correlated with morphology. The quantitative cell
classifiers eliminate the subjectivity of human evaluation, giving
comparisons between patients a degree of confidence previously
unattainable. Longitudinal studies will also benefit greatly by the
quantitative analysis, and the ability to digitally store and
retrieve large numbers of cellular image files, particularly as
compared to prior art techniques for the retrieval of microscope
slides and/or digital photographs of relatively small numbers of
cells.
Discrimination of Morphological Features Using Fluorescence-Based
Methodologies
A technology employed in detection of cancer cells in a bodily
fluid based on image data of a population of cells from the bodily
fluid was the development of preliminary absorbance and
fluorescence staining protocols for simultaneous morphological
analysis of bright field and fluorescence imagery.
Initially, investigations considered the simultaneous use of
chromogenic stains and fluorescent dyes. The ability of the imaging
system discussed above to produce bright field imagery, as well as
multiple colors of fluorescence imagery of each cell, raised the
possibility of simultaneously employing both traditional
chromogenic stains and fluorescent dyes for analysis. However,
because chromogenic stains do not normally penetrate cell membranes
of viable cells, and because the optical systems discussed above
are able to collect laser side scatter imagery, it was determined
that much of the information on cell granularity that was
traditionally acquired via stains, such as Eosin, could be obtained
using laser side scatter imagery, without the need for cell
staining. Numerous cell-permeant fluorescent dyes offer nuclear
morphology without the need for fixing and chromogenic staining.
Based on these considerations, it was determined that
fluorescence-based alternatives for discrimination of morphological
image features provide a better approach than traditional staining
methodologies.
The primary fluorescence-based alternatives to chromogenic stains
useful in conjunction with the optical systems discussed above are
fluorescent DNA binding dyes. A wide variety of such dyes are
excitable at 488 nm, including several SYTO dyes (Molecular
Probes), DRAQ5 (BioStatus), 7-AAD, Propidium Iodide (PI), and
others. These dyes are alternatives to chromogenic nuclear stains
such as Toluidine Blue, Methyl Green, Crystal Violet, Nuclear Fast
Red, Carmalum, Celestine Blue, and Hematoxylin. A fluorescent DNA
binding dye is generally included in assay protocols developed for
use with the optical systems described above, for the purposes of
defining the shape and boundaries of the nucleus, its area, its
texture (analogous to heterochromaticity), as well as to provide
DNA content information.
IDEAS.TM., the software image analysis program discussed above,
enables evaluation of combinations of image features from different
images of the same cell, in order to expand the utility of the
fluorescence nuclear image. For example, the nuclear image mask can
be subtracted from the bright field image mask (which covers the
entire cell) as a means for generating a mask that includes only
the cytoplasmic region. Once defined, the cytoplasmic mask can be
used to calculate the cytoplasmic area, the N/C ratio, the relative
fluorescence intensity of probes in the cytoplasm and nucleus,
etc., via an intuitive "Feature Manager." An example of a Feature
Manager session for the definition of the N/C ratio is shown in
FIG. 4. Basic image features associated with any cell image are
selected from a list and combined algebraically using a simple
expression builder.
Measurement of Photometric and Morphometric Parameters
In an exemplary implementation of the concepts disclosed herein,
ImageStream.TM. data analysis and cell classification are performed
post-acquisition using the IDEAS.TM. software package. An annotated
IDEAS.TM. software screen capture of an analysis of human
peripheral blood is shown in FIG. 5. The IDEAS.TM. software enables
the visualization and photometric/morphometric analysis of data
files containing imagery from tens of thousands of cells, thereby
combining quantitative image analysis with the statistical power of
flow cytometry.
The exemplary screen shot of FIG. 5 includes images and
quantitative data from 20,000 human peripheral blood mononuclear
cells. Whole blood was treated with an erythrocyte lysing agent,
and the cells were labeled with an anti-CD45-PerCP mAb (red) and a
DNA binding dye (green). Each cell was imaged in fluorescence using
the FL1 and FL4 spectral bands, as well as dark field and bright
field. Images of a plurality of cells in a dark field channel 51a,
a green fluorescent channel 51b, a bright field channel 51c, and a
red fluorescent channel 51d can readily be identified in this
Figure. Such a thumbnail image gallery (in the upper left of the
interface) enables the "list mode" inspection of any population of
cells. Cell imagery can be pseudo-colored and superimposed for
visualization in the image gallery or enlarged, as shown at the
bottom of the interface, for four different cell types (eosinophils
53a, NK cells 53b, monocytes 53c, and neutrophils 53d).
The software also enables one- and two-dimensional plotting of
image features calculated from the imagery. Dots 55 that represent
cells in the two-dimensional plots can be "clicked" to view the
associated imagery in the gallery. The reverse is true as well.
Cell imagery can be selected to highlight the corresponding dot in
every plot in which that cell appears. In addition, gates 57 can be
drawn on the plots to define subpopulations, which can then be
inspected in the gallery using a "virtual cell sort" functionality.
Any image feature calculated from the imagery or defined by the
user (i.e., selected from a list of basic and automatically
combined algebraically using a simple expression builder) can be
plotted. A dot plot 59a (displayed at the center left of FIG. 5)
shows the clustering resulting from an analysis of CD45 expression
(x-axis) versus a dark field granularity metric (y-axis), which is
similar to side-scatter intensity measured in conventional flow
cytometry. Plot 59a reveals lymphocytes (green in a full color
image), monocytes (red in a full color image), neutrophils
(turquoise in a full color image), and eosinophils (orange in a
full color image). A dot plot 59b (displayed at the center right of
FIG. 5) substitutes a nuclear texture parameter, "nuclear
frequency" for CD45 expression on the x-axis, revealing a putative
NK cell population (purple in a full color image). Back-displaying
the purple population on the left dot plot reveals that this
population has the same mean CD45 expression as the lymphocyte
population (green on a full color image). The frequency parameter
is one member of the morphologic and photometric image feature set
that was developed and incorporated into the IDEAS.TM. software
package. Table 1 below provides an exemplary listing of exemplary
photometric and morphometric definitions that can be identified for
every image (or subpopulation, as appropriate). It should be
recognized that FIG. 5 has been modified to facilitate its
reproduction. As a full-color image, the background of each frame
including a cell is black, and the background for each dot plot is
black, to facilitate visualization of the cells and data.
TABLE-US-00001 TABLE 1 Morphometric and Photometric Definitions
Image Features Description of Parameters for Each Image (6 per
object) Area Area of mask in pixels Aspect Ratio Aspect ratio of
mask Aspect Ratio Intensity Intensity-weighted aspect ratio of mask
Background Mean Intensity Mean intensity of pixels outside of mask
Background StdDev Intensity Standard deviation of intensity of
pixels outside of mask Centroid X Centroid of mask in horizontal
axis Centmid X Intensity Intensity-weighted centroid of mask in
horizontal axis Centroid Y Centroid of mask in vertical axis
Centmid Y Intensity Intensity-weighted centroid of mask in vertical
axis Combined Mask Intensity Total intensity of image using logical
"OR" of all six image masks Frequency Variance of intensity of
pixels within mask Gradient Max Maximum intensity gradient of
pixels within mask Gradient RMS RMS of intensity gradient of pixels
within mask Intensity Background-corrected sum of pixel intensities
within mask Major Axis Major axis of mask in pixels Major Axis
Intensity Intensity-weighted major axis of mask in pixels Mean
Intensity Total Intensity of image divided by area of mask Minimum
Intensity Minimum pixel intensity within mask Minor Axis Minor axis
of mask in pixels Minor Axis Intensity Intensity-weighted minor
axis of mask in pixels Object Rotation Angle Angle of major axis
relative to axis of flow Object Rotation Angle Intensity Angle of
intensity-weighted major axis relative to axis of flow Peak
Intensity Maximum pixel intensity within mask Perimeter Number of
edge pixels in mask Spot Large Max Maximum pixel intensity within
large bright spots Spot Large Total Sum of pixel intensities within
large bright spots Spot Medium Max Maximum pixel intensity within
medium-sized bright spots Spot Medium Total Sum of pixel
intensities within medium-sized bright spots Spot Raw Max
Un-normalized maximum pixel intensity within large bright spots
Spot Raw Total Sum of un-normalized pixel intensities within large
bright spots Spot Small Max Maximum pixel intensity within small
bright spots Spot Small Total Sum of pixel intensities within small
bright spots Total Intensity Sum of pixel intensities within mask
Spot Count Number of spots detected in image Combined Mask Area
Area of logical `OR" of all six image masks in pixels Flow Speed
Camera line readout rate in Hertz at time object was imaged Object
Number Unique object number Similarity Pixel intensity correlation
between two images of the same object User-Defined Features Any
algebraic combination of imagery and masks User-Defined Masks
Erode, dilate, threshold, Boolean combinations User-Defined
Populations Any Boolean combination of defined populations
Image features that quantitate morphology are shown in italics in
Table 1. Each image feature is automatically calculated for all six
types of images (dark field, bright field, and four fluorescent
images, that are simultaneously captured) for each cell, when an
image data set is loaded into the software.
Over 35 image features are calculated per image, which amounts to
over 200 image features per cell in assays that employ all six
images, not including user-defined image features. Each cell is
also assigned a unique serial number and time stamp, enabling
kinetic studies over cell populations.
Selection of a Photometric/Morphometric Image Features for
Carcinoma Cells
It was initially proposed that bladder epithelial cells would be
used to investigate morphometric differences between normal and
epithelial carcinoma cells. However, the initial samples of bladder
washings that were analyzed revealed that the cell number per
sample was highly variable, and generally too low to be practical
for use in the ImageStream.TM. instrument. Mammary epithelial cells
were therefore used in place of bladder cells. Mammary cells were
chosen because normal, primary cells of this kind are commercially
available (Clonetics/InVitrogen) and will expand as adherent cells
in short-term tissue culture with specialized growth media. In
addition, mammary epithelial carcinoma cells derived from breast
cancer metastases are available from the American Type Tissue
Culture Collection (ATCC). In order to better control for tumor to
tumor variability, three different mammary epithelial carcinoma
cell lines were studied: HCC-1 500, HCC-1 569, and HCC-1428. These
lines were established from metastases in three separate patients
and were purchased from ATCC as frozen stocks. The cell lines grew
adherent to plastic, were expanded by routine tissue culture
methods, and used experimentally.
Normal and cancerous mammary epithelial cells were harvested
separately by brief incubation with trypsin/EDTA at 37 degrees
Celsius. The cells were washed once in cold phosphate buffer
solution (PBS) containing 1% FCS, counted, and used experimentally.
The three separate mammary epithelial carcinoma cell lines were
pooled in equal proportions for the experiments described
below.
Normal mammary epithelial cells were stained with a
fluorescein-conjugated monoclonal antibody to the HLA Class I MHC
cell surface protein by incubating the cells with the appropriate,
predetermined dilution of the mAb for 30 minutes at 4 degrees C.
Despite the fact that mammary carcinomas are known to down-regulate
Class I MHC expression, as a precaution, the normal cells were
fixed in 1% paraformaldehyde to limit passive transfer to the
carcinoma cells. The combined mammary carcinoma cells lines were
also fixed in 1% paraformaldehyde and added to the normal mammary
cell population. DRAQ5 (BioStatus, Ltd, Leicestershire, UK), a DNA
binding dye that can be excited with a 488 nm laser and emits in
the red waveband, was added to the sample prior to running on the
ImageStream.TM. instrument. The labeling of normal mammary
epithelial cells with anti-Class I MHC mAb enabled the normal cells
to be identified in mixes of normal and carcinoma cells, thereby
providing an objective "truth" to facilitate the identification of
image features distinguishing normal epithelial cell from
epithelial carcinoma cells.
Normal peripheral blood was obtained from AlICells (San Diego,
Calif.). Whole blood was incubated with FITC conjugated anti-CD45
mAb, which is expressed at some level on all peripheral white blood
cells. Red blood cells were then lysed by incubation of the whole
blood in a Becton Dickinson FACSLyse.TM. for 3 minutes at room
temperature. The cells were washed in PBS, counted and fixed with
1% paraformaldehyde. Mammary epithelial carcinoma cells were
prepared as above, fixed in 1% paraformaldehyde and added to the
peripheral blood cells. DRAQ5 was then added as a nuclear stain,
and the cells were run on the ImageStream.TM. instrument.
Image files containing image data of the cell mixes described above
(normal mammary epithelial cells mixed with mammary carcinoma
cells, and normal peripheral blood cells mixed with mammary
carcinoma cells) were analyzed using the IDEAS.TM. software package
with the results described below.
After performing spectral compensation on the data file, an initial
visual inspection was performed to compare normal mammary
epithelial cells (positive for anti-HLA-FITC) to the carcinoma
cells (unstained for anti-HLA-FITC). Representative images of
normal cells are shown in FIG. 6, while representative images of
carcinoma cells are shown in FIG. 7. In each Figure, each
horizontal row includes four simultaneously acquired images of a
single cell. Images in columns 61a and 71a correspond to blue laser
side scatter images (i.e., dark field images), images in columns
61b and 71b correspond to green HLA-FITC fluorescence images,
images in columns 61c and 71c correspond to bright field images,
and images in columns 61d and 71d correspond to red nuclear
fluorescence. As described above, the preferred imaging system is
capable of simultaneously collecting six different types of images
of a single cell (a dark field image, a bright field image, and
four fluorescence images); in FIGS. 6 and 7, two of the
fluorescence channels have not been utilized. It should be
recognized that FIGS. 6 and 7 have been modified to facilitate
their reproduction. As full-color images, the backgrounds of FIGS.
6 and 7 are black, images in columns 61a and 71a are blue, images
in columns 61b and 71b are green, images in columns 61c and 71c are
grayscale images on a gray background, and images in columns 61d
and 71d are red.
When visually comparing full-color images of FIGS. 6 and 7, it is
immediately apparent that images of normal mammary epithelial cells
in column 61c (the green fluorescence channel) of FIG. 6 are vivid,
while images of carcinoma cells in column 71c (the green
fluorescence channel) of FIG. 7 can hardly be distinguished. It is
also apparent that while none of the dark field images (columns 61a
and 71a) are particularly intense, the dark field images (column
61a) of normal mammary epithelial cells in FIG. 6 are significantly
more intense than are the dark field images (column 71a) of
carcinoma cells in FIG. 7. Yet another qualitative observation that
can be readily made is that the average intensity of the red
fluorescence images (column 71d) of carcinoma cells in FIG. 7 is
substantially greater than the average intensity of the red
fluorescence images (column 61a) in FIG. 6. Further qualitative
observations indicate that normal cells have higher heterogeneity,
were generally larger, and had lower nuclear intensity. The
subsequent analysis sought to quantitate these differences, as well
as to discover additional parameters that might have discrimination
capability. A screen capture of the corresponding IDEAS.TM.
analysis is shown in FIG. 8A.
The analysis shown in FIG. 8A proceeded from a dot plot 81 in FIG.
8B. Single cells were first identified, based on dot plot 81, which
was defined as bright field area versus aspect ratio. A gate (not
separately shown) was drawn around the population containing
putative single cells based on the criteria of the area being
sufficiently large to exclude debris, and the aspect ratio being
greater than -0.5, which eliminates doublets and clusters of cells.
The veracity of the gating was tested by examining random cells
both within and outside of the gate using the click-on-a-dot
visualization functionality.
Next, the normal mammary cells were distinguished from the mammary
carcinoma cells using the anti-HLA-FITC marker that was applied
only to the normal cells. A solid yellow histogram 85a of FITC
intensity was generated and is shown in FIG. 8C. A gate 83 was then
drawn around the FITC positive (normal mammary epithelial cells)
and FITC negative (mammary epithelial carcinoma cells), resulting
in a subpopulation of 2031 normal cells, and a subpopulation of 611
carcinoma cells. These subpopulations were then used to identify
image features that quantitatively discriminated between normal and
cancerous cells, based on differential histograms. It should be
recognized that FIG. 8A has been modified to facilitate its
reproduction. As a full-color image, the background of each frame
including a cell is black, and the background for each dot plot and
histogram is black, to facilitate visualization of the cells and
data. This modification resulted in the even distribution of dots
81a, even though such an even distribution was not present in the
full color image.
The remaining ten histograms (i.e., histograms 85b-85k) shown in
FIGS. 8D-8M are differential histograms of the normal cells 87a
(shown as green in a full-color image) and carcinoma cells 87b
(shown as red in a full-color image), with each histogram
representing a different quantitative image feature. The ten
discriminating image features fell into five distinct classes:
scatter intensity, scatter texture, morphology, nuclear intensity,
and nuclear texture. Differential histograms 85b, 85c, and 85d
demonstrate the difference between the two populations using three
different, but correlated, scatter intensity image features:
"scatter mean intensity" (total intensity divided by cell area),
"scatter intensity" (total intensity minus background), and
"scatter spot small total" (total intensity of local maxima).
Although all three scatter intensity image features provided good
discrimination, "scatter mean intensity" (histogram 85b) was the
most selective.
Differential histograms 85e and 85f quantitated scatter texture
using either an intensity profile gradient metric ("scatter
gradient RMS"; histogram 85e) or the variance of pixel intensities
("scatter frequency"; histogram 85f), which proved more
selective.
Differential histograms 85g, 85h and 85i plotted the cellular area
(bright field area, histogram 85g), nuclear area (from the DNA
fluorescence imagery, histogram 85h), and cytoplasmic area
(cellular/nuclear area, histogram 85i). The carcinoma cell lines
were generally smaller in bright field area, confirming the
qualitative observations from cell imagery. While the nuclear area
of the carcinoma cell lines was proportionately smaller than that
of the normal cells (e.g., the Nuclear/Cellular area ratio was not
discriminatory), the cytoplasmic area was significantly lower in
the carcinoma cells.
Finally, differential histograms 85j and 85k plotted the nuclear
mean intensity (histogram 85j) and nuclear frequency
(heterochromaticity, histogram 85k), respectively. As in the case
of scatter, both of these image features provided some
discriminatory power.
The multispectral/multimodal imagery collected by the
ImageStream.TM. instrument and analyzed using the IDEAS.TM.
software package in this engineered experiment revealed a number of
significant differences in dark field scatter, morphology, and
nuclear staining between normal epithelial and epithelial carcinoma
cells. While it is well-recognized that cells adapted to tissue
culture have undergone a selection process that may have altered
their cellular characteristics, these data demonstrate that it is
feasible to build an automated classifier that uses the
morphometric and photometric image features identified and
described above to separate normal from transformed epithelial
cells, and possibly other cell types.
A further experimental investigation analyzed image data collected
from a mixture of normal peripheral blood cells and mammary
carcinoma cells. As shown in FIG. 5 (discussed above), cell
classification of human peripheral blood can be achieved using a
flow imaging system configured to simultaneously obtain a plurality
of images of each cell, and using an automatic image analysis
program (with the ImageStream.TM. instrument representing an
exemplary imaging system, and the IDEAS.TM. software package
representing an exemplary image analysis program). Using CD45
expression combined with an analysis of dark field light scatter
properties, cells can be separated into five distinct populations
based on the image data collected by the flow imaging system:
lymphocytes, monocytes, neutrophils, eosinophils and basophils This
separation of human peripheral blood into distinct subpopulations
is shown in greater detail in FIG. 9, which includes exemplary
relative abundance data for the different subpopulations. The
veracity of the classifications was determined by using
population-specific monoclonal antibody markers and backgating
marker-positive cells on the scatter vs. CD45 plot, as well as
morphological analysis of the associated imagery. The x-axis of the
graph in FIG. 9 corresponds to anti-CD45-FITC Intensity, while the
y-axis corresponds to dark field scatter intensity.
In order to determine whether the techniques disclosed herein
(utilizing the flow imaging instrument system described above,
which is exemplified by the ImageStream.TM. instrument, and imaging
analysis software, which is exemplified by the IDEAS.TM. software
package) could discriminate epithelial carcinoma cells from normal
PBMC, an artificial mixture of tumor cells and normal PBMC was
produced as described above. The cell mixture was labeled with an
anti-CD45-FITC mAb and a fluorescent DNA binding dye in order to
differentiate PBMC subpopulations, generally as described above. A
comparison of the scatter vs. CD45 bivariate plots for normal
peripheral blood mononuclear cells and the PBMC sample spiked with
the carcinoma cells is shown in FIGS. 10A and 10B. FIG. 10A
graphically illustrates a distribution of normal peripheral blood
mononuclear cells (PBMC) based on image data collected from a
population of cells that does not include mammary carcinoma cells.
FIG. 10B graphically illustrates a distribution of normal PBMC and
mammary carcinoma cells based on image data collected from a
population of cells that includes both cell types, illustrating how
the distribution of the mammary carcinoma cells is distinguishable
from the distribution of the normal PBMC cells. In this analysis,
carcinoma cells 101a fall well outside of a normally defined PBMC
population 101b, as confirmed by visual inspection of the outlier
population.
As shown in FIGS. 11A and 11B, carcinoma cells 111a can also be
discriminated from normal PBMC 111b using some of the morphometric
and photometric image features identified in FIG. 8A (e.g., nuclear
area, cytoplasmic area, scatter intensity, and scatter frequency).
FIG. 11A graphically illustrates a distribution of normal PBMC and
mammary carcinoma cells based on measured cytoplasmic area derived
from image data collected from a population of cells that includes
both cell types, illustrating how the distribution of cytoplasmic
area of mammary carcinoma cells is distinguishable from the
distribution of cytoplasmic area of the normal PBMC cells. FIG. 11B
graphically illustrates a distribution of normal PBMC and mammary
carcinoma cells based on measured scatter frequency derived from
image data collected from a population of cells that includes both
cell types, illustrating how the distribution of the scatter
frequency of the mammary carcinoma cells is distinguishable from
the distribution of the scatter frequency of the normal PBMC cells.
Although these image features were initially identified for the
purpose of discriminating between normal mammary and mammary
carcinoma cells, they provide a high level of discrimination
between mammary epithelial carcinoma cells and PBMC. Significantly,
normal epithelial cells would be even more clearly differentiated
from PBMC and distinct from the epithelial carcinoma cells using
these parameters.
It should be recognized that FIGS. 10A, 10B, 11A, and 11B have been
modified to facilitate their reproduction. As a full-color images,
the background of each frame including a dot plot is black, to
facilitate visualization of the cells and/or data, and dots
representing PBMC cells and carcinoma cells are different
colors.
The results noted above were verified by visual inspection of the
segregated images (i.e., the images separated into subpopulations
corresponding to carcinoma cells and healthy cells using one or
more of the above identified photometric and/or morphometric
parameters). Image gallery data were produced from the spiked PBMC
data described above. FIG. 12 includes representative images from
the carcinoma cell population, obtained using an overlay composite
of bright field and DRAQ5 DNA fluorescence (red, with the image
processing being performed by the image analysis software). FIG. 13
includes images of the five peripheral blood mononuclear cell
populations defined using dark field scatter, CD45 (green), and
DRAQ5 (red) for nuclear morphology. Note that the two Figures are
at different size scales. It should be recognized that FIG. 12 has
been modified to facilitate its reproduction. As a full-color
image, the background of FIG. 12 is black, the background of each
frame including a cell is brown/grey, and the nucleus of each is
cell is red. FIG. 13 has been similarly modified to facilitate its
reproduction. As a full-color image, the background of FIG. 13 is
black, the periphery of each cell is green, and the nucleus of each
is cell is red.
Significantly, the above studies demonstrate the feasibility of
optically discriminating a subpopulation of normal epithelial cells
from a subpopulation of transformed cells by analyzing
multi-spectral/multimodal image data from a mixed population of
such cells, where the image data are simultaneously collected. The
above studies also demonstrate the feasibility of detecting
epithelial carcinoma cells in blood by analyzing
multi-spectral/multimodal image data from a mixed population of
such cells, where the image data are simultaneously collected.
With respect to applying the concepts described herein to a
specific disease condition concept, because of the relatively high
operating speed of the exemplary imaging system (.about.100
cells/second or .about.350,000 cells/hour), and because of the
relatively large amount of image information collected for each
cell (high resolution bright field image, dark field image, and
four fluorescence images), it is believed that the concept
disclosed herein is particularly suitable for the detection and
monitoring of chronic lymphocytic leukemia.
In such an application, a 360 nm UV laser will be incorporated into
the simultaneous multispectral/multimodal imaging system, and the
optics of the imaging system will be optimized for
diffraction-limited imaging performance in the 400-460 nm (DAPI
emission) spectral band. The exemplary imaging system used in the
empirical studies detailed above (i.e., the ImageStream.TM.
instrument) employs a solid state, 200 mW, 488 nm laser for
fluorescence excitation. While such a laser wavelength excites a
broad range of fluorochromes, it is not optimal for cell cycle
analysis due to its inability to excite DAPI, which binds
stoichiometrically to DNA. In addition, the beam is configured to
have a narrow width, which improves overall sensitivity in exchange
for increased measurement variation from cell to cell. Feasibility
studies employing propidium iodide as a DNA stain indicate that the
imaging system employing the 488 nm laser can generate cell cycle
histograms having G0/G1 peak coefficients of variation of
.about.5%.
In order to generate high resolution cell cycle histograms for the
detection of changes in DNA content associated with CLL, the DAPI
optimized 360 nm UV laser will instead be used. The beam will be
configured to have a relatively wide illumination cross-section
(.about.100 microns), so that under typical operating conditions,
DAPI excitation consistency will be within 1% from cell to cell.
Overall, cell cycle histogram CV is expected to be about 2-3%. In
addition, the optics in the exemplary instrument used in the
empirical studies discussed above are diffraction-limited from
460-750 nm, which does not cover the DAPI spectral emission band.
Thus, such optics will be replaced with optics that are configured
to achieve diffraction-limited imaging performance in the 400-460
nm spectral band, in order to measure detailed nuclear
characteristics of diagnostic value, such as notched morphology and
heterochromaticity.
Particularly for use with applying the concept disclosed herein for
the detection of changes in DNA content associated with CLL, it
would be desirable to provide image processing software
incorporating additional masking algorithms and image features that
define nuclear morphology in normal samples, beyond those described
above.
The morphometric image feature set available in the exemplary image
processing software discussed above does not include boundary
contour image features that quantitate nuclear lobicity, number of
invaginations, and similar parameters. Because such image features
capture many of the qualitative observations of nuclear morphology
traditionally used by hematopathologists, they would be of
extremely high utility in the analysis of leukocytes. Incorporation
of such algorithms and image features would enable improved
automated classification of normal cells, precursors, and
transformed cells.
The boundary contour masking algorithm and associated image
features employed in the empirical studies discussed above improve
cell classification between eosinophils, neutrophils, monocytes,
basophils, and lymphocytes in about 1/3 of cells of each type, as a
function of their orientation with respect to the imaging plane.
Cells that are not in one of two preferred orientations (out of six
possible orientations) do not benefit from the previously employed
algorithm and image features. To improve the cell classification,
the boundary contour algorithm and image features can be extended
to consistently classify normal leukocytes, independent of their
rotational orientation, which will lead to a first-pass classifier
between normal and transformed cells, by increasing the statistical
resolution between the expected locations of normal cell
distributions, thereby improving the ability to flag abnormal cells
that fall outside the expected positions. Such a classifier will
also enable the image features to be characterized for the
morphologic differences observed between normal and transformed
lymphocytes, to further improve discrimination, using the
techniques generally discussed above.
To configure the imaging analysis software for the detection of
changes in DNA content associated with CLL, an automated classifier
will be incorporated into the software package. The automated
classifier will incorporate at least one or more of the following
photometric and/or morphometric parameters: DNA content, nuclear
morphology, heterochromaticity, N/C ratio, granularity, CD45
expression, and other parameters. As discussed above, the
classifier will be configured to analyze image data corresponding
to a population of blood cells, to classify the population into the
following subpopulations: lymphocytes, monocytes, basophils,
neutrophils, and eosinophils.
Automated differential analysis of PBMC based on multimodal imagery
simultaneously collected from cells in flow will be performed using
imaging systems consistent with those described above, and imaging
processing software consistent with those described above. PBMC
will be stained with FITC conjugated anti-CD45 and the DNA binding
dye, DAPI. Peripheral blood leukocytes will be classified in a
five-part differential analysis into lymphocytes, monocytes,
basophils, neutrophils, and eosinophils, generally as indicated in
FIGS. 5, 9, and 13.
Data sets from peripheral blood leukocytes from CLL patients will
be acquired and analyzed, as discussed above. The classification
scheme developed for normal peripheral blood leukocytes will be
applied to these data sets, and the identification of CLL cells
will be determined by comparison with normal profiles. Various
classifiers will be evaluated to determine which segments CLL cells
best exemplify, generally as described above with respect to the
histograms of FIG. 8. Among these will be: cell size, nuclear size,
nuclear to cytoplasmic ratio, nuclear contour, nuclear texture, and
cytoplasmic granules. Results will be compared with standard blood
films from CLL patient samples to determine the veracity of the
technique.
In addition to the normal staining protocol utilizing anti-CD45 as
a marker, peripheral blood leukocytes will be stained with
monoclonal antibodies to CD5 and CD2O, plus DAPI, before image data
are collected. This approach will enable the identification of the
CLL cells according to accepted flow cytometric criteria. In this
way, morphologic criteria can be correlated with the
immunophenotype.
Analyzing large (10,000 to 20,000 white blood cell) data sets from
multiple CLL patients will facilitate the optimization and
selection of photometric and morphometric image features that can
be used classify blood cells by subpopulation (i.e., lymphocytes,
monocytes, basophils, neutrophils, and eosinophils).
Morphological heterogeneity has been observed in CLL cells;
however, an accurate objective appreciation of the degree of this
has not been achieved due to the technical difficulty of preparing
and assessing peripheral blood films from patients consistently.
Acquisition of large data sets from CLL patients using the
multimodal imaging systems discussed above will enable the
objective analysis of the degree of morphological heterogeneity by
the imaging processing software package. The classifier(s)
developed above will be applied to these data sets, and
morphological heterogeneity assessed by analyzing the degree to
which the particular classifier (e.g., nuclear size, N/C ratio,
etc.) applies across the large populations of CLL cells. Based on
this analysis, the classifier that most accurately identifies the
greatest percentage of CLL cells will be optimized, so that the
entire population is included by the classifier.
As noted above, when applied to CLL, the techniques disclosed
herein are not being used to separate a population of cells into a
subpopulation corresponding to healthy cells, and a subpopulation
corresponding to diseased cells. Instead, image data collected from
a population of blood cells will be used to separate the population
of blood cells into subpopulations based on blood cell type (i.e.,
lymphocytes, monocytes, basophils, neutrophils, and eosinophils)
Because CLL is associated with an increase in the amount of
lymphocytes present in the blood cell population (i.e., an increase
in the lymphocytes subpopulation), detecting an increase in
lymphocytes provides an indication of the existence of the disease
condition (i.e., CLL). While the preferred method described herein
involves separating the blood cell population into a plurality of
different subpopulations, it should be recognized that a CLL
detection technique could be implemented simply by separating the
blood cell population into a lymphocyte subpopulation and a
non-lymphocyte subpopulation. Using empirical data representing
average lymphocyte subpopulations in healthy patients, detection of
a higher-than-average lymphocyte subpopulation provides an
indication of a CLL disease condition.
In addition to initially detecting the CLL disease condition, the
imaging and analysis techniques discussed in detail above can be
applied to follow patients with CLL longitudinally to determine
their response to treatment, stability of the clinical response,
and disease relapse. Changes in peripheral blood populations,
including both normal and any residual CLL, can be followed and
correlated with clinical outcome.
Exemplary Computing Environment
As noted above, an aspect of the present invention involves image
analysis of a plurality of images simultaneously collected from
members of the population of cells. Reference has been made to an
exemplary image analysis software package. FIG. 14 and the
following related discussion are intended to provide a brief,
general description of a suitable computing environment for
practicing the present invention, where the image processing
required is implemented using a computing device generally like
that shown in FIG. 14. Those skilled in the art will appreciate
that the required image processing may be implemented by many
different types of computing devices, including a laptop and other
types of portable computers, multiprocessor systems, networked
computers, mainframe computers, hand-held computers, personal data
assistants (PDAs), and on other types of computing devices that
include a processor and a memory for storing machine instructions,
which when implemented by the processor, result in the execution of
a plurality of functions.
An exemplary computing system 150 suitable for implementing the
image processing required in the present invention includes a
processing unit 154 that is functionally coupled to an input device
152, and an output device 162, e.g., a display. Processing unit 154
include a central processing unit (CPU 158) that executes machine
instructions comprising an image processing/image analysis program
for implementing the functions of the present invention (analyzing
a plurality of images simultaneously collected for members of a
population of objects to enable at least one characteristic
exhibited by members of the population to be measured). In at least
one embodiment, the machine instructions implement functions
generally consistent with those described above, with reference to
the flowchart of FIG. 3, as well as the exemplary screenshots.
Those of ordinary skill in the art will recognize that processors
or central processing units (CPUs) suitable for this purpose are
available from Intel Corporation, AMD Corporation, Motorola
Corporation, and from other sources.
Also included in processing unit 154 are a random access memory 156
(RAM) and non-volatile memory 160, which typically includes read
only memory (ROM) and some form of memory storage, such as a hard
drive, optical drive, etc. These memory devices are
bi-directionally coupled to CPU 158. Such storage devices are well
known in the art. Machine instructions and data are temporarily
loaded into RAM 156 from non-volatile memory 160. Also stored in
memory are the operating system software and ancillary software.
While not separately shown, it should be understood that a power
supply is required to provide the electrical power needed to
energize computing system 150.
Input device 152 can be any device or mechanism that facilitates
input into the operating environment, including, but not limited
to, a mouse, a keyboard, a microphone, a modem, a pointing device,
or other input devices. While not specifically shown in FIG. 14, it
should be understood that computing system 150 is logically coupled
to an imaging system such as that schematically illustrated in FIG.
1, so that the image data collected are available to computing
system 150 to achieve the desired image processing. Of course,
rather than logically coupling the computing system directly to the
imaging system, data collected by the imaging system can simply be
transferred to the computing system by means of many different data
transfer devices, such as portable memory media, or via a network
(wired or wireless). Output device 162 will most typically comprise
a monitor or computer display designed for human visual perception
of an output image.
Comparison of Two Cell Populations to Evaluate Patient Health
As discussed in detail above, image data for a plurality of images
of individual cells that are acquired simultaneously can be used to
detect a disease condition. Note that such an application of the
present approach is based on identifying and/or quantifying
differences between a first cell population and a second cell
population, by analyzing the image data collected for each cell
population. Generally, as described above, the image data can be
analyzed to identify quantifiable photometric and morphometric
differences between the first and second cell populations. The
image data can also be used to identify a cell type present in one
of the first and second cell populations, but not in the other of
the first and second cell populations. Similarly, the image data
can also be used to identify differences in the relative numbers of
cell types in the first and second cell populations, to determine
if there are more or less of a particular cell type in the first
population of cells, as compared to the second population of cells
(and vice versa). These techniques can provide diagnostic
information about a patient from whom the cells are obtained,
beyond simply determining if a specific disease condition
exists.
For example, assume a patient provides a first sample of blood or
bodily fluid taken on a first date, and image data from that first
population of cells are generated as described above. Image data
from a subsequent sample (i.e., a second population of cells) taken
on a later date can be compared to the image data from the first
population of cells to identify differences between those
populations. it may be possible to correlate those differences to
some phenomenon occurring between the collection of the first
population of cells and the second population of cells. By way of
example, such phenomena can include, but are not limited to,
exposure to stress conditions (such an analysis will enable
researchers to better understand cellular reactions to specific
stress factors, such as heat, cold, exercise, mental stress,
emotional stress, etc.), exposure to radiation (such an analysis
will enable researchers to better understand cellular reactions to
specific types of radiation), a change in diet (such an analysis
will enable researchers to better understand cellular reactions to
specific types of dietary changes), a change in lifestyle (such an
analysis will enable researchers to better understand cellular
reactions to specific types of lifestyle changes), a change in a
patient's use of nutritional supplements (such an analysis will
enable researchers to better understand cellular reactions to the
use of specific nutritional supplements), types of dietary changes,
and a change in a patient's use of medications (such an analysis
will enable researchers to better understand cellular reactions to
the use of specific medications). While some such phenomena may be
related to a specific disease condition, other phenomena may more
generally be related to a patient's health and/or well being.
In the example provided immediately above, the first and second
population of cells were obtained from a person at different times.
It should also be understood that the first and second population
of cells can be obtained from a person at the same time, but then
treated differently before being imaged as described above. For
example, a single blood sample or bodily fluid sample can be
acquired from a person, and that sample can be split into two
fractions, one for the first population and the other fraction for
the second population. Image data for the first fraction (the first
population of cells) can then be acquired. The second fraction (the
second population of cells) can be exposed to one of more
phenomena, such as those noted above, and then imaged (note that
the second fraction can be manipulated and/or exposed to some
stimulus other than those specifically identified above). The image
data from the first and second populations of cells can then be
analyzed to determine how the cell populations differ as a result
of changes in the second population caused by the phenomena,
manipulation, or stimulation.
FIG. 15 is a flow chart 401 schematically illustrating exemplary
steps that can be used to analyze two populations of cells based on
images of the cell populations, in order to identify and/or
quantify differences between the cell populations. In a
particularly preferred exemplary embodiment, each cell population
is obtained from a bodily fluid, such as blood. In a block 403, an
imaging system, such as the exemplary imaging system described in
detail above, is used to collect image data from a first population
of biological cells. In a block 405, the imaging system is also
used to collect image data from a second population of biological
cells. As noted above, the first and second cell populations can be
acquired at different times, or may be obtained from a single
sample acquired at one time and the sample then split into two
fractions. In a block 407, the image data from the two cell
populations are computationally analyzed (using a processor, such
as provided by a computing device or an application specific
circuit, or other logic device) to identify differences between the
cell populations. In at least some exemplary embodiments, the
differences are quantified in terms of at least one photometric or
morphometric image feature. In terms of the exemplary IDEAS.TM.
software discussed above, the analysis can either look for specific
changes identified by a user (such as a change in the relative
abundance of cell types between the two populations), or the
analysis can look for any and all differences that are identifiable
based on the image data, and then rank those differences (in terms
of morphological image features, photometric image features, and
cell abundance) in order of their significance. Generally, as
discussed above, the use of one or more fluorescent labels can
facilitate the comparative analysis.
The high level steps of FIG. 15 can be used for many different
types of investigations. The following provides a brief description
of six different investigations that can be performed consistent
with the exemplary steps of FIG. 15.
A first investigation analyzes image data from a first population
of cells and a second population of cells to determine if any
variation in a specific cell type present in both populations is
indicative of a disease condition. This technique is described in
significant detail above in the specification, in the context of
using the first population to identify disease related image
features, and looking for such image features in the second
population.
A second investigation analyzes image data from a first population
of cells and a second population of cells to determine if any
variation exists for a specific cell type present in both
populations, regardless of whether the difference is indicative of
a disease condition. This technique is generally directed at
acquiring the first and second cell populations from a person at
different times, and determining if there is any difference between
the same cell type in the first and second populations. If data are
available regarding conditions experienced by the person during the
time between acquiring the samples, then an attempt can be made to
correlate the changes to such conditions. Even where no correlation
can be found, any change identified may be indicative of the health
of the person. For example, some cellular changes may suggest that
the health of the patient has improved or declined, even if no
specific disease condition is identified. Furthermore, even if no
change in the first and second cell populations is identified, that
finding may itself comprise valuable diagnostic data, either
indicating that the health of the person has not appreciably
changed, or if the person's health has changed, indicating that the
specific cell type is likely not related to the change in
health.
A third investigation analyzes image data from a first population
of cells and a second population of cells to determine if there has
been a change in the relative distributions of different types of
cells present in both populations, where such a change can be
indicative of a disease condition. This analysis will include
determining if a specific cell type is present in the first cell
population but not the second cell population, and vice versa, as
well as determining how the relative percentage of cell types
present in both the first and second cell populations has changed.
This technique is described in significant detail above in the
specification, in the context of using relative cell abundance to
determine if a disease condition is indicated.
A fourth investigation also analyzes image data from a first
population of cells and a second population of cells to determine
if there has been a change in the relative abundance of different
types of cells present in both populations, where such a change is
not limited to indicating a specific disease condition, but may
still be relevant to the health of the person from whom the first
and second cell populations were obtained. Again, this analysis
includes determining if a specific cell type is present in the
first cell population but not the second cell population, and vice
versa, as well as determining how the relative percentage of cell
types present in both the first and second cell populations has
changed. This technique is generally directed at acquiring the
first and second cell populations from a person at different times,
and determining if there are differences between the distributions
of different cell types in the first and second populations. If
data are available indicating conditions experienced by the person
during the time between acquiring the samples, then an attempt can
be made to correlate the changes to such conditions. Even where no
correlation can be found, any changes identified may be indicative
of the health of the person. For example, some cellular
distribution changes may suggest that the health of the patient has
improved or declined, even if no specific disease condition is
identified. Furthermore, even if no change in the cellular
distributions in the first and second cell populations is
identified, that fact itself may comprise valuable diagnostic data,
either indicating that the health of the person has not appreciably
changed, or if the person's health has changed, indicating that the
cellular distribution is likely not related to the change in
health.
A fifth investigation analyzes image data from a first population
of cells and a second population of cells to determine how the
second population of cells responds to a stimulus not applied to
the first population of cells, in order to detect a disease
condition. In general, this technique is based on acquiring one
sample from a person, and splitting that sample into two different
fractions (the two different cell populations could be acquired
from the person at different times; however, doing so will
introduce an additional variable). The first population of cells
serves as a control. A stimulus is applied to the second population
of cells. The term "stimulus" should be broadly interpreted as
something likely to induce a change in the second population of
cells relative to the first population of cells. By way of example,
such a stimulus can include, but is not limited to, exposing the
second population to a change in temperature, exposing the second
population to a reagent, exposing the second population to
radiation, exposing the second population to a change in
environmental conditions, and exposing the second population to a
drug. In general, the first population of cells will not be exposed
to the stimulus. It should be noted that the first population of
cells may be manipulated in some fashion to enable changes between
the population of cells to be more readily apparent, such as
labeling the first population of cells.
A sixth investigation, similar to the fifth investigation discussed
above, analyzes image data from a first population of cells and a
second population of cells to determine how the second population
of cells responds to a stimulus not applied to the first population
of cells. The sixth investigation differs from the fifth in that a
change detected may not be indicative of a specific disease
condition, while still being relevant to the health of the person
from whom the populations of cells were obtained. Furthermore, as
generally discussed above, even where the comparison of the first
population to the second population does not indicate any
significant changes, that information may in itself be relevant to
the health of the person.
Although the concepts disclosed herein have been described in
connection with the preferred form of practicing them and
modifications thereto, those of ordinary skill in the art will
understand that many other modifications can be made thereto within
the scope of the claims that follow. Accordingly, it is not
intended that the scope of these concepts in any way be limited by
the above description, but instead be determined entirely by
reference to the claims that follow.
* * * * *